AI hallucinations are not inherently harmful — in fact, in certain creative and artistic contexts, they can be incredibly interesting — but they are statements that aren’t grounded in fact or logic and should be treated as such.

Some AI hallucinations are easy to spot, while others may be more subtle and go undetected. If users fail to identify an AI hallucination when it occurs and so pass off this output as fact, it can lead to a range of issues. In any case, if your work relies on an generative AI program, it’s essential to prevent AI hallucinations – see our 7 methods below.

Understanding AI Hallucinations

AI models — especially generative AI models and LLMs — have grown in the complexity of the data and queries they can handle, generating intelligent responses, imagery, audio, and other outputs that typically align with the users’ requests and expectations.

However, artificial intelligence models aren’t foolproof; because of the massive amounts of training data, complicated algorithms, and other less-than-transparent factors that go into preparing these models for the market, many AI platforms have run into issues where they “hallucinate” and deliver incorrect information.

For instance, in this example, ChatGPT attempts to translate “Lojban” – which is a fake language – into English, with semi-hilarious results:

However, an AI hallucination is not always a negative thing, as long as you know what you’re dealing with; certain AI hallucinations, especially with audio and visuals, can lead to interesting elements for creative brainstorming and project development.

7 Methods for Detecting and Preventing AI Hallucinations

The following techniques are designed for AI model developers and vendors as well as for organizations that are deploying AI as part of their business operations, but not necessarily developing their own models.

Please note: These techniques are not focused on the actual inputs users may be putting into the consumer-facing sides of these models. But if you’re a consumer looking to optimize AI outcomes and avoid hallucinatory responses, here are a few quick tips:

• Submit clear and focused prompts that stick to one core topic; you can always follow up with a separate query if you have multiple parts to your question.
• Ensure you provide enough contextual information for the AI to provide an accurate and well-rounded response.
• Avoid acronyms, slang, and other confusing language in your prompts.
• Verify AI outputs with your own research.
• Pay attention; in many cases, AI hallucinations give themselves away with odd or outlandish language or images.

Now, here’s a closer look at how tech leaders and organizations can prevent, detect, and mitigate AI hallucinations in the AI models they develop and manage:

1) Clean and Prepare Training Data for Better Outcomes

Appropriately cleansing and preparing your training data for AI model development and fine-tuning is one of the best steps you can take toward avoiding AI hallucinations.

A thorough data preparation process improves the quality of the data you’re using and gives you the time and testing space to recognize and eliminate issues in the dataset, including certain biases that could feed into hallucinations.

Data preprocessing, normalization, anomaly detection, and other big data preparation work should be completed from the outset and in some form each time you update training data or retrain your model. For retraining in particular, going through data preparation again ensures that the model has not learned or retained any behavior that will feed back into the training data and lead to deeper problems in the future.

2) Design Models With Interpretability and Explainability

The larger AI models that so many enterprises are moving toward have massive capabilities but can also become so dense with information and training that even their developers struggle to interpret and explain what these models are doing.

Issues with interpretability and explainability become most apparent when models begin to produce hallucinatory or inaccurate information. In this case, model developers aren’t always sure what’s causing the problem or how they can fix it, which can lead to frustration within the company and among end users.

To remove some of this doubt and confusion from the beginning, plan to design AI models with interpretability and explainability, incorporating features that focus on these two priorities into your blueprint design. While building your own models, document your processes, maintain transparency among key stakeholders, and select an architecture format that is easy to interpret and explain, no matter how data and user expectations grow.

One type of architecture that works well for interpretability, explainability, and overall accuracy is an ensemble model; this type of AI/ML approach pulls predicted outcomes from multiple models and aggregates them for more accurate, well-rounded, and transparent outputs.

3) Test Models and Training Data for Performance Issues

Before you deploy an AI model and even after the fact, your team should spend significant time testing both the AI models and any training data or algorithms for performance issues that may arise in real-world scenarios.

Comprehensive testing should cover not only more common queries and input formats but also edge cases and complex queries. Testing your AI on how it responds to a wide range of possible inputs predicts how the model will perform for different use cases. It also gives your team the chance to improve data and model architecture before end users become frustrated with inaccurate or hallucinatory results.

If the AI model you’re working with can accept data in different formats, be sure to test it both with alphanumeric and audio or visual data inputs. Also, consider completing adversarial testing to intentionally try to mislead the model and determine if it falls for the bait. Many of these tests can be automated with the right tools in place.

4) Incorporate Human Quality Assurance Management

Several data quality, AI management, and model monitoring tools can assist your organization in maintaining high-quality AI models that deliver the best possible outputs. However, these tools aren’t always the best for detecting more obscure or subtle AI hallucinations; in these cases and many others, it’s a good idea to include a team of humans who can assist with AI quality assurance management.

Using a human-in-the-loop review format can help to catch oddities that machines may miss and also give your AI developers real-world recommendations for how improvements should be made. The individuals who handle this type of work should have a healthy balance of AI/technology skills and experience, customer service experience, and perhaps even compliance experience. This blended background will give them the knowledge they need to identify issues and create better outcomes for your end users.

5) Collect User Feedback Regularly

Especially once an AI model is already in operation, the users themselves are your best source of information when it comes to AI hallucinations and other performance aberrations. If appropriate feedback channels are put in place, users can inform model developers and AI vendors of real scenarios where the model’s outputs went amiss.

With this specific knowledge, developers can identify both one-off outcomes and trending errors, and, from there, they can use this knowledge to improve the model’s training data and responses to similar queries in future iterations of the platform.

6) Partner With Ethical and Transparent Vendors

Whether you’re an AI developer or an enterprise that uses AI technology, it’s important to partner with other ethical vendors that emphasize transparent and compliant data collection, model training, model design, and model deployment practices.

This will ensure you know how the models you use are trained and what safeguards are in place to protect user data and prevent hallucinatory outcomes. Ideally, you’ll want to work with vendors that can clearly articulate the work they’re doing to achieve ethical outcomes and produce products that balance accuracy with scalability.

To gain a deeper understanding of AI ethical issues, read our guide: Generative AI Ethics: Concerns and Solutions

7) Monitor and Update Your Models Over Time

AI models work best when they are continuously updated and improved. These improvements should be made based on user feedback, your team’s research, trends in the greater industry, and any performance data your quality management and monitoring tools collect. Regularly monitoring AI model performance from all these angles and committing to improving models based on these analytics can help you avoid previous hallucination scenarios and other performance problems in the future.

How and Why Do AI Hallucinations Occur?

It’s not always clear how and why AI hallucinations occur, which is part of why they have become such a problem. Users aren’t always able to identify hallucinations when they happen and AI developers often can’t determine what anomaly, training issue, or other factor may have led to such an outcome.

The algorithms on which modern neural networks and larger AI models are trained are highly complex and designed to mimic the human brain. This gives them the ability to handle more complex and multifaceted user requests, but it also gives them a level of independence and seeming autonomy that makes it more difficult to understand how they arrive at certain decisions.

While it does not appear that AI hallucinations can be eliminated at this time, especially in more intricate AI models, these are a few of the most common issues that contribute to AI hallucinations:

• Incomplete training data.
• Biased training data.
• Overfitting and lack of context.
• Unclear or inappropriately sized model parameters.
• Unclear prompts.

Issues That May Arise From AI Hallucinations

AI hallucinations can lead to a number of different problems for your organization, its data, and its customers. These are just a handful of the issues that may arise based on hallucinatory outputs:

• Inaccurate decision-making and diagnostics: AI instances may confidently make an inaccurate statement of fact that leads healthcare workers, insurance providers, and other professionals to make inaccurate decisions or diagnoses that negatively impact other people and/or their reputations. For example, based on a query it receives about a patient’s blood glucose levels, an AI model may diagnose a patient with diabetes when their blood work does not indicate this health problem exists.
• Discriminatory, offensive, harmful, or otherwise outlandish outputs: Whether it’s the result of biased training data or the rationale is completely obscure, an AI model may suddenly begin to generate harmfully stereotypical, rude, or even threatening outputs. While these kinds of outlandish outputs are typically easy to detect, they can lead to a range of issues, including offending the end user.
• Unreliable data for analytics and other business decisions: AI models aren’t always perfect with numbers, but instead of stating when they are unable to come to the correct answer, some AI models have hallucinated and produced inaccurate data results. If business users are not careful, they may unknowingly rely on this inaccurate business analytics data when making important decisions.
• Ethical and legal concerns: AI hallucinations may expose private data or other sensitive information that can lead to cybersecurity and legal issues. Additionally, offensive or discriminatory statements may lead to ethical dilemmas for the organization that hosts this AI platform.
• Misinformation related to global news and current events: When users work with AI platforms to fact-check, especially for real-time news and ongoing current events, depending on how the question is phrased and how recent and comprehensive the AI model’s training is, the model may confidently produce misinformation that the user may spread without realizing its inaccuracies.
• Poor user experience: If an AI model regularly produces offensive, incomplete, inaccurate, or otherwise confusing content, users will likely become frustrated and choose to stop using the model and/or switch to a competitor. This can alienate your core audience and limit opportunities for building a larger audience of users.

Read next: 50 Generative AI Startups to Watch

Bottom Line: Preventing AI Hallucinations When Using Large-Scale AI Models

The biggest AI innovators recognize that AI hallucinations create real problems and are taking major steps to counteract hallucinations and misinformation, but AI models continue to produce hallucinatory content on occasion.

Whether you’re an AI developer or an enterprise user, it’s important to recognize that these hallucinations are happening, but, fortunately, there are steps you can take to better identify hallucinations and correct for the negative outcomes that accompany them. It requires the right combination of comprehensive training and testing, monitoring and quality management tools, well-trained internal teams, and a process that emphasizes continual feedback loops and improvement. With this strategy in place, your team can better address and mitigate AI hallucinations before they lead to cybersecurity, compliance, and reputation issues for the organization.

For more information about governing your AI deployment, read our guide: AI Policy and Governance: What You Need to Know

The post What Are AI Hallucinations and How To Stop Them appeared first on eWEEK.

Both of these application and service suites are in heavy demand, as organizations seek to harness real-time repositories of big data for various enterprise use cases, including artificial intelligence and machine learning model development and deployment.

When choosing between two of the most highly regarded data platforms on the market, users often have difficulty differentiating between Cognos and Power BI and weighing each of the platform’s pros and cons. In this in-depth comparison guide, we’ll compare these two platforms across a variety of qualities and variables to assess where their strengths lie.

But first, here’s a glance at the areas where each tool excels most:

• Cognos Analytics: Best for advanced data analytics and on-premises deployment. Compared to Power BI, Cognos is particularly effective for advanced enterprise data analytics use cases that require more administrative controls over security and governance. Additionally, it is more reliable when it comes to processing large quantities of data quickly and accurately.
• Power BI: Best for affordable, easy-to-use, integrable BI technology in the cloud. Compared to Cognos Analytics, Power BI is much more versatile and will fit into the budget, skill sets, and other requirements of a wider range of teams. Most significant, this platform offers free access versions that are great for teams that are just getting started with this type of technology.

Cognos vs. Power BI at a Glance

What Is Cognos?

Cognos Analytics is a business intelligence suite of solutions from IBM that combines AI-driven assistance, advanced reporting and analytics, and other tools to support various enterprise data management requirements. The platform is available both in the cloud and on demand for on-premises and custom enterprise network configurations.

With its range of features, Cognos enables users to connect, verify, and combine data and offers plenty of dashboard and visualization options. Cognos is particularly good at pulling and analyzing corporate data, providing detailed reports, and assisting in corporate governance. It is built on a strong data science foundation and is supported by heavy-duty analytics and recommendations, courtesy of IBM Watson.

Also see: Top Business Intelligence Software

Key Features of Cognos

• AI-driven insights: The platform benefits from veteran AI support in the form of Watson, which helps with data visualization design, dashboard builds, forecasting, and data explainability. This is particularly helpful for users with limited data science and coding experience who need to pull in-depth analyses from complex datasets.
• Data democratization through natural language: Advanced natural language capabilities make it possible for citizen data scientists and less-experienced tech professionals to create accurate and detailed data visualizations.
• Advanced reporting and dashboarding: Multi-user reports and dashboards, personalized report generation, AI-powered dashboard design, and easy shareability make this a great platform for organizations that require different levels of data visibility and granularity for different stakeholders.
• Automation and governance: Extensive automation and governance capabilities help power users scale their operations without compromising data security. The platform’s robust governance and security features are important to highly regulated businesses and large enterprises in particular.

Pros

• The platform is well integrated with other business tools, like Slack and various email inboxes, making it easier to collaborate and share insights across a team.
• Its AI assistant works well for a variety of data analytics and management tasks, even for users with no data science experience, because of its natural language interface.
• Cognos comes with flexible deployment options, including on-demand cloud, hosted cloud, and client hosting for either on-premises or IaaS infrastructure.

Cons

• The platform is not particularly mobile-friendly compared to similar competitors.
• While a range of visuals are available on the platform, many user reviews indicate that the platform’s visuals are limited and not very customizable.
• Depending on your exact requirements, Cognos Analytics can become quite expensive, especially if you have a high user count or require more advanced features like security and user management.

What Is Power BI?

Microsoft Power BI is a business intelligence and data visualization software solution that acts as one part of the Microsoft Power Platform. Because of its unification with other Power Platform products like Power Automate, Power Apps, and Power Pages, this BI tool gives users diverse low-code and AI-driven operations for more streamlined data analytics and management. Additional integrations with the likes of Microsoft 365, Teams, Azure, and SharePoint are a major selling point, as many business users are already highly invested in these business applications and are familiar with the Microsoft approach to UX/UI.

Specific to analytics functions, Power BI focuses most heavily on data preparation, data discovery, dashboards, and data visualization. Its core features enable users to take visualizations to the next level and empower them to make data-driven decisions, collaborate on reports, and share insights across popular applications. They can also create and modify data reports and dashboards easily and share them securely across applications.

Key Features of Power BI

• Rapidly expanding AI analytics: AI-powered data analysis and report creation have already been established in this platform, but recently, the generative AI Copilot tool has also come into preview for Power BI. This expands the platform’s ability to create reports more quickly, summarize and explain data in real time, and generate DAX calculations.
• CRM integration: Power BI integrates relatively well with Microsoft Dynamics CRM, which makes it a great option for in-depth marketing and sales analytics tasks. Many similar data platforms do not offer such smooth CRM integration capabilities.
• Embedded and integrated analytics: The platform is available in many different formats, including as an embedded analytics product. This makes it possible for users of other Microsoft products to easily incorporate advanced analytics into their other most-used Microsoft products. You can also embed detailed reports in other apps for key stakeholders who need information in a digestible format.
• Comprehensive visualizations: Adjustable dashboards, AI-generated and templated reports, and a variety of self-service features enable users to set up visuals that can be alphanumeric, graphical, or even include geographic regions and maps. Power BI’s many native visualization options mean users won’t have to spend too much time trying to custom-fit their dashboards and reports to their company’s specific needs.

Pros

• Power BI is one of the more mobile-friendly data platforms on the market today.
• In addition to its user-friendly and easy-to-learn interface, Microsoft offers a range of learning resources and is praised for its customer support.
• Its AI-powered capabilities continue to grow, especially through the company’s close partnership with OpenAI.

Cons

• Some users have commented on the tool’s outdated interface and how data updates, especially for large amounts of data, can be slow and buggy.
• The platform, especially the Desktop tool, uses a lot of processing power, which can occasionally lead to slower load times and platform crashes.
• Shareability and collaboration features are incredibly limited outside of its highest paid plan tier.

Best for Core Features: It Depends

It’s a toss-up when it comes to the core features Cognos Analytics and Power BI bring to the table.

Microsoft Power BI’s core features include a capable mobile interface, AI-powered analytics, democratized report-building tools and templates, and intuitive integrations with other Microsoft products.

IBM Cognos Analytics’ core features include a web-based report authoring tool, natural-language and AI-powered analytics, customizable dashboards, and security and access management capabilities. Both tools offer a variety of core features that work to balance robustness and accessibility for analytics tasks.

To truly differentiate itself, Microsoft consistently releases updates to its cloud-based services, with notable updates and feature additions over the past couple of years including AI-infused experiences, smart narratives (NLG), and anomaly detection capabilities. Additionally, a Power BI Premium version enables multi-geography capabilities and the ability to deploy capacity to one of dozens of data centers worldwide.

On the other hand, IBM has done extensive work to update the Cognos home screen, simplifying the user experience and giving it a more modern look and feel. Onboarding for new users has been streamlined with video tutorials and accelerator content organized in an easy-to-consume format. Additionally, improved search capabilities and enhancements to the Cognos AI Assistant and Watson features help generate dashboards automatically, recommend the best visualizations, and suggest questions to ask — via natural language query — to dive deeper into data exploration.

Taking these core capabilities and recent additions into account, which product wins on core features? Well, it depends on the user’s needs. For most users, Power BI is a stronger option for general cloud and mobility features, while Cognos takes the lead on advanced reporting, data governance, and security.

Also see: Top Dashboard Software & Tools

Best for Ease of Use and Implementation: Power BI

Although it’s close, new users of these tools seem to find Power BI a little easier to use and set up than Cognos Analytics.

As the complexity of your requirements rises, though, the Power BI platform grows more difficult to navigate. Users who are familiar with Microsoft tools will be in the best position to use the platform seamlessly, as they can take advantage of skills from applications they already use, such as Microsoft Excel, to move from building to analyzing to presenting with less data preparation. Further, all Power BI users have access to plenty of free learning opportunities that enable them to rapidly start building reports and dashboards.

Cognos, on the other hand, has a more challenging learning curve, but IBM has been working on this, particularly with recent user interface updates, guided UI for dashboard builds, and assistive AI. The tool’s AI-powered and Watson-backed analytics capabilities in particular lower the barrier of entry to employing advanced data science techniques.

The conclusion: Power BI wins on broad usage by a non-technical audience, whereas IBM has the edge with technical users and continues to improve its stance with less-technical users. Overall, Power BI wins in this category due to generally more favorable user reviews and commentary about ease of use.

Also see: Top AI Software

Best for Advanced Analytics Capabilities: Cognos

Cognos Analytics surpasses Power BI for its variety of in-depth and advanced analytics operations.

Cognos integrates nicely with other IBM solutions, like the IBM Cloud Pak for Data platform, which extends the tool’s already robust data analysis and management features. It also brings together a multitude of data sources as well as an AI Assistant tool that can communicate in plain English, sharing fast recommendations that are easy to understand and implement. Additionally, the platform generates an extensive collection of visualizations. This includes geospatial mapping and dashboards that enable the user to drill down, rise, or move horizontally through visuals that are updated in real time.

Recent updates to Cognos’s analytical capabilities include a display of narrative insights in dashboard visualizations to show meaningful aspects of a chart’s data in natural language, the ability to specify the zoom level for dashboard viewing and horizontal scrolling in visualizations, as well as other visualization improvements.

On the modeling side of Cognos, data modules can be dynamically redirected to different data server connections, schemas, or catalogs at run-time. Further, the Convert and Relink options are available for all types of referenced tables, and better web-based modeling has been added.

However, it’s important to note that Cognos still takes a comparatively rigid, templated approach to visualization, which makes custom configurations difficult or even impossible for certain use cases. Additionally, some users say it takes extensive technical aptitude to do more complex analysis.

Power BI’s strength is out-of-the-box analytics that doesn’t require extensive integration or data science smarts. It regularly adds to its feature set. More recently, it has added new features for embedded analytics that enable users to embed an interactive data exploration and report creation experience in applications such as Dynamics 365 and SharePoint.

For modeling, Microsoft has added two new statistical DAX functions, making it possible to simultaneously filter more than one table in a remote source group. It also offers an Optimize ribbon in Power BI Desktop to streamline the process of authoring reports (especially in DirectQuery mode) and more conveniently launch Performance Analyzer to analyze queries and generate report visuals. And while Copilot is still in preview at this time, this tool shows promise for advancing the platform’s advanced analytics capabilities without negatively impacting its ease of use.

In summary, Power BI is good at crunching and analyzing real-time data and continues to grow its capabilities, but Cognos Analytics maintains its edge, especially because Cognos can conduct far deeper analytics explorations on larger amounts of data without as many reported performance issues.

Also see: Data Analytics Trends

Best for Cloud Users: Power BI; Best for On-Prem Users: Cognos

Both platforms offer cloud and on-premises options for users, but each one has a clear niche: Power BI is most successful on the cloud, while Cognos has its roots in on-prem setups.

Power BI has a fully functional SaaS version running in Azure as well as an on-premises version in the form of Power BI Report Server. Power BI Desktop is also offered for free as a standalone personal analysis tool.

Although Power BI does offer on-prem capabilities, power users who are engaged in complex analysis of multiple on-premises data sources typically still need to download Power BI Desktop in addition to working with Power BI Report Server. The on-premises product is incredibly limited when it comes to dashboards, streaming analytics, natural language, and alerting.

Cognos also offers both cloud and on-premises versions, with on-demand, hosted, and flexible on-premises deployment options that support reporting, dashboarding, visualizations, alters and monitoring, AI, and security and user management, regardless of which deployment you choose. However, Cognos’ DNA is rooted in on-prem, so it lags behind Microsoft on cloud-based bells and whistles.

Therefore, Microsoft gets the nod for cloud analytics, and Cognos for on-prem, but both are capable of operating in either format.

Also see: Top Data Visualization Tools

Best for Integrations: It Depends

Both Cognos Analytics and Power BI offer a range of helpful data storage, SaaS, and operational tool integrations that users find helpful. Ultimately, neither tool wins this category because they each have different strengths here.

Microsoft offers an extensive array of integration options natively, as well as APIs and partnerships that help to make Power BI more extensible. Power BI is tightly embedded into much of the Microsoft ecosystem, which makes it ideally suited for current Azure, Dynamics, Microsoft 365, and other Microsoft customers. However, the company is facing some challenges when it comes to integrations beyond this ecosystem, and some user reviews have reflected frustrations with that challenge.

IBM Cognos connects to a large number of data sources, including spreadsheets. It is well integrated into several parts of the vast IBM portfolio. It integrates nicely, for example, with the IBM Cloud Pak for Data platform and more recently has added integration with Jupyter notebooks. This means users can create and upload notebooks into Cognos Analytics and work with Cognos Analytics data in a notebook using Python scripts. The platform also comes with useful third-party integrations and connectors for tools like Slack, which help to extend the tool’s collaborative usage capabilities.

This category is all about which platform and IT ecosystem you live within, so it’s hard to say which tool offers the best integration options for your needs. Those invested in Microsoft will enjoy tight integration within that sphere if they select Power BI. Similarly, those who are committed to all things IBM will enjoy the many ways IBM’s diverse product and service set fit with Cognos.

Also see: Digital Transformation Guide: Definition, Types & Strategy

Best for Pricing: Power BI

While Cognos Analytics offers some lower-level tool features at a low price point, Power BI offers more comprehensive and affordable entry-level packages to its users.

Microsoft is very good at keeping prices low as a tactic for growing market share. It offers a lot of features at a relatively low price. Power BI Pro, for example, costs approximately $10 per user per month, while the Premium plan is $20 per user per month. Free, somewhat limited versions of the platform are also available via Power BI Desktop and free Power BI accounts in Microsoft Fabric.

The bottom line for any rival is that it is hard to compete with Microsoft Power BI on price, especially because many of its most advanced features — including automated ML capabilities and AI-powered services — are available in affordable plan options.

IBM Cognos Analytics, on the other hand, has a reputation for being expensive. It is hard for IBM to compete with Power BI on price alone.

IBM Cognos Analytics pricing starts at $10 per user per month for on-demand cloud access and $5 per user per month for limited mobile user access to visuals and alerts on the cloud-hosted or client-hosted versions. For users who want more than viewer access and the most basic of capabilities, pricing can be anywhere from $40 to $450 per user per month.

Because of the major differences in what each product offers in its affordable plans, Microsoft wins on pricing.

Also see: Data Mining Techniques

Why Shouldn’t You Use Cognos or Power BI?

While both data and BI platforms offer extensive capabilities and useful features to users, it’s possible that these tools won’t meet your particular needs or align with industry-specific use cases in your field. If any of the following points are true for your business, you may want to consider an alternative to Cognos or Power BI:

Who Shouldn’t Use Cognos

The following types of users and companies should consider alternatives to Cognos Analytics:

• Users or companies with smaller budgets or who want a straightforward, single pricing package; Cognos tends to have up-charges and add-ons that are only available at an additional cost.
• Users who require extensive customization capabilities, particularly for data visualizations, dashboards, and data exploration.
• Users who want a more advanced cloud deployment option.
• Users who have limited experience with BI and data analytics technology; this tool has a higher learning curve than many of its competitors and limited templates for getting started.
• Users who are already well established with another vendor ecosystem, like Microsoft or Google.

Who Shouldn’t Use Power BI

The following types of users and companies should consider alternatives to Power BI:

• Users who prefer to do their work online rather than on a mobile device; certain features are buggy outside of the mobile interface.
• Users who are not already well acquainted and integrated with the Microsoft ecosystem may face a steep learning curve.
• Users who prefer to manage their data in data warehouses rather than spreadsheets; while data warehouse and data lake integrations are available, including for Microsoft’s OneLake, many users run into issues with data quality in Excel.
• Users who prefer a more modern UI that updates in real time.
• Users who primarily use Macs and Apple products; some users have reported bugs when attempting to use Power BI Desktop on these devices.

Also see: Best Data Analytics Tools

If Cognos or Power BI Isn’t Ideal for You, Check Out These Alternatives

While Cognos and Power BI offer extensive features that will meet the needs of many BI teams and projects, they may not be the best fit for your particular use case. The following alternatives may prove a better fit:

Domo

Domo puts data to work for everyone so they can extend their data’s impact on the business. Underpinned by a secure data foundation, the platform’s cloud-native data experience makes data visible and actionable with user-friendly dashboards and apps. Domo is highly praised for its ability to help companies optimize critical business processes at scale and quickly.

Yellowfin

Yellowfin is a leading embedded analytics platform that offers intuitive self-service BI options. It is particularly successful at accelerating data discovery. Additionally, the platform allows anyone, from an experienced data analyst to a non-technical business user, to create reports in a governed way.

Wyn Enterprise

Wyn Enterprise offers a scalable embedded business intelligence platform without hidden costs. It provides BI reporting, interactive dashboards, alerts and notifications, localization, multitenancy, and white-labeling in a variety of internal and commercial apps. Built for self-service BI, Wyn offers extensive visual data exploration capabilities, creating a data-driven mindset for the everyday user. Wyn’s scalable, server-based licensing model allows room for your business to grow without user fees or limits on data size.

Zoho Analytics

Zoho Analytics is a top BI and data analytics platform that works particularly well for users who want self-service capabilities for data visualizations, reporting, and dashboarding. The platform is designed to work with a wide range of data formats and sources, and most significantly, it is well integrated with a Zoho software suite that includes tools for sales and marketing, HR, security and IT management, project management, and finance.

Sigma

Sigma is a cloud-native analytics platform that delivers real-time insights, interactive dashboards, and reports, so you can make data-driven decisions on the fly. With Sigma’s intuitive interface, you don’t need to be a data expert to dive into your data, as no coding or SQL is required to use this tool. Sigma has also recently brought forth Sigma AI features for early access preview.

Review Methodology

The two products in this comparison guide were assessed through a combination of reading product materials on vendor sites, watching demo videos and explanations, reviewing customer reviews across key metrics, and directly comparing each product’s core features through a comparison graph.

Below, you will see four key review categories that we focused on in our research. The percentages used for each of these categories represent the weight of the categorical score for each product.

User experience – 30%

Our review placed a heavy emphasis on user experience, considering both ease of use and implementation as well as the maturity and reliability of product features. We looked for features like AI assistance and low-code/no-code capabilities that lessened the learning curve, as well as learning materials, tutorials, and consistent customer support resources. Additionally, we paid attention to user reviews that commented on the product’s reliability and any issues with bugs, processing times, product crashes, or other performance issues.

Advanced analytics and scalability – 30%

To truly do business intelligence well, especially for modern data analytics requirements, BI tools need to offer advanced capabilities that scale well. For this review, we emphasized AI-driven insights, visuals that are configurable and updated in real time, shareable and collaborative reports and dashboards, and comprehensive features for data preparation, data modeling, and data explainability. As far as scalability goes, we not only looked at the quality of each of these tools but also assessed how well they perform and process data on larger-scale operations. We particularly highlighted any user reviews that mentioned performance lag times or other issues when processing large amounts of data.

Integrations and platform flexibility – 20%

Because these platforms need to be well integrated into a business’s data sources and most-used business applications to be useful, our assessment also paid attention to how integrable and flexible each platform was for different use cases. We considered not only how each tool integrates with other tools from the same vendor but also which data sources, collaboration and communication applications, and other third-party tools are easy to integrate with native integrations and connectors. We also considered the quality of each tool’s APIs and other custom opportunities for integration, configuration, and extensibility.

Affordability – 20%

While affordability is not the be-all-end-all when it comes to BI tools, it’s important to many users that they find a tool that balances an accessible price point with a robust feature set. That’s why we also looked at each tool’s affordability, focusing on entry price points, what key features are and are not included in lower-tier pricing packages, and the jumps in pricing that occur as you switch from tier to tier. We also considered the cost of any additional add-ons that users might need, as well as the potential cost of partnering with a third-party expert to implement the software successfully.

Bottom Line: Cognos vs. Power BI

Microsoft is committed to investing heavily in Power BI and enhancing its integrations across other Microsoft platforms and a growing number of third-party solutions. Any organization that is a heavy user of Office 365, Teams, Dynamics, and/or Azure will find it hard to resist the advantages of deploying Power BI.

And those advantages are only going to increase. On the AI front, for example, the company boasts around 100,000 customers using Power BI’s AI services. It is also putting effort into expanding its AI capabilities, with the generative AI-driven Copilot now in preview for Power BI users. For users with an eye on their budget who don’t want to compromise on advanced analytics and BI features, Power BI is an excellent option.

But IBM isn’t called Big Blue for nothing. It boasts a massive sales and services team and global reach into large enterprise markets. It has also vastly expanded its platform’s AI capabilities, making it a strong tool for democratized data analytics and advanced analytics tasks across the board.

Where Cognos Analytics has its most distinct advantage is at the high end of the market. Microsoft offers most of the features that small, midsize, and larger enterprises need for analytics. However, at the very high end of the analytics market, and in corporate environments with hefty governance and reporting requirements or legacy and on-premises tooling, Cognos has carved out a strategic niche that it serves well.

Ultimately, either tool could work for your organization, depending on your budget, requirements, and previous BI tooling experience. The most important step you can take is to speak directly with representatives from each of these vendors, demo these tools, and determine which product includes the most advantageous capabilities for your team.

Read next: 10 Best Machine Learning Platforms

The post Cognos vs. Power BI: 2024 Data Platform Comparison appeared first on eWEEK.

As two well-regarded data analytics platforms in the BI space, users may have a difficult time deciding between Looker and Power BI for their data management requirements. There are arguments for and against each, and in this comparison guide, we’ll dive deeper into core features, pros, cons, and pricing for Looker and Power BI.

But before we go any further, here’s a quick summary of how each product stands out against its competitors:

• Looker: Best for current Google product users and others who are most interested in highly configurable and advanced analytics capabilities, including data visualizations and reporting. Looker Studio in particular balances ease of use with high levels of customization and creativity, while also offering users a lower-cost version of an otherwise expensive platform.
• Power BI: Best for current Microsoft product users and others who want an easy-to-use and affordable BI tool that works across a variety of data types and use cases. This is considered one of the most popular BI tools on the market and meets the needs of a variety of teams, budgets, and experience levels, though certain customizations and big data processing capabilities are limited.

Looker vs. Power BI at a Glance

What Is Looker?

Looker is an advanced business intelligence and data management platform that can be used to analyze and build data-driven applications, embed data analytics in key organizational tools, and democratize data analysis in a way that preserves self-service capabilities and configurability. The platform has been managed by Google since its acquisition in 2019, and because of its deep integration within the Google ecosystem, it is a favorite among Google Cloud and Workspace users for unified analytics projects. However, the tool also works well with other cloud environments and third-party applications, as it maintains a fairly intuitive and robust collection of integrations.

Key features of Looker

• Comprehensive data visualization library: In addition to giving users the ability to custom-configure their visualizations to virtually any parameters and scenarios, Looker’s data visualization library includes a wide range of prebuilt visual options. Traditional visuals like bar graphs and pie charts are easy to access, and more complex visuals — like heatmaps, funnels, and timelines — can also be easily accessed.
• “Blocks” code snippets: Instead of reinventing the wheel for certain code snippets and built-out data models, Looker Blocks offers prebuilt data models and code to help users quickly develop high-quality data models. Industry-specific, cloud-specific, and data-source-specific blocks are all available, which makes this a great solution for users of all backgrounds who want to get started with complex models more quickly.
• Governed and integrated data modeling: With its proprietary modeling language and emphasis on Git-driven data storage and rule development, users can easily build trusted and governed data sources that make for higher-quality and more accurate data models, regardless of how many teams are working off of these models.

Pros

• Looker comes with a large library of prebuilt integrations — including for many popular data tools — and also offers user-friendly APIs for any additional integrations your organization may need to set up.
• Looker’s visualizations and reports are easy to customize to your organization’s more specific project requirements and use cases; it also offers one of the more diverse visualization libraries in this market.
• LookML allows users to create centralized governance rules and handle version control tasks, ensuring more accurate outcomes and higher quality data, even as data quantities scale.

Cons

• On-premises Looker applications do not easily connect to Looker Studio and other cloud-based tools in user portfolios, which severely limits the ability to maintain data projects accurately and in real time for on-prem users.
• Looker uses its own modeling language, which can make it difficult for new users to get up and running quickly.
• Some users have had trouble with self-service research and the vendor’s documentation.

What Is Power BI?

Microsoft Power BI is a business intelligence and data visualization solution that is one of the most popular data analytics tools on the market today. As part of the Microsoft Power Platform, the tool is frequently partnered with Microsoft products like Power Automate, Power Apps, and Power Pages to get the most out of data in different formats and from different sources. Its focus on ease of use makes it a leading option for teams of all backgrounds; especially with the growth of its AI-powered assistive features, visualization templates, and smooth integrations with other Microsoft products, it has become one of the best solutions for democratized data science and analytics.

Key features of Power BI

• AI-driven analytics: AI-powered data analysis and report creation have already been established in this platform, but recently, the generative AI Copilot tool has also come into preview for Power BI. This expands the platform’s ability to create reports more quickly, summarize and explain data in real time, and generate DAX calculations.
• Dynamics 365 integration: Power BI integrates relatively well with the Microsoft Dynamics CRM, which makes it a great option for in-depth marketing and sales analytics tasks. Many similar data platforms do not offer such smooth CRM integration capabilities.
• Comprehensive mobile version: Unlike many other competitors in this space, Microsoft Power BI comes with a full-featured, designed-for-mobile mobile application that is available at all price points and user experience levels. With native mobile apps available for Windows, iOS, and Android, any smartphone user can quickly review Power BI visualizations and dashboards from their personal devices.

Pros

• Power BI can be used in the cloud, on-premises, and even as an embedded solution in other applications.
• The user interface will be very familiar to users who are experienced with Microsoft products; for others, the platform is accompanied by helpful training resources and ample customer support.
• This platform makes democratized data analytics simpler, particularly with AI-powered features and a growing generative AI feature set.

Cons

• While some users appreciate that Power BI resembles other Microsoft 365 office suite interfaces, other users have commented on the outdated interface and how it could be improved to look more like other cloud-based competitors.
• Especially with larger quantities of data, the platform occasionally struggles to process data quickly and accurately; slower load times, crashes, and bugs are occasionally introduced during this process.
• Visualizations are not very customizable, especially compared to similar competitors.

Best for Core Features: It Depends

Both Looker and Power BI offer all of the core features you would expect from a data platform, including data visualizations, reporting and dashboarding tools, collaboration capabilities, and integrations. They also offer additional features to assist users with their analytical needs. Power BI offers support through AI assistance and Looker supports users with prebuilt code snippets and a diverse integration and plugin marketplace.

Microsoft maintains a strong user base with its full suite of data management features and easy-to-setup integrations with other Microsoft tools. It can be deployed on the cloud, on-premises, and in an embedded format, and users can also access the tool via a comprehensive mobile application.

Looker is web-based and offers plenty of analytics capabilities that businesses can use to explore, discover, visualize, and share analyses and insights. Enterprises can use it for a wide variety of complex data mining techniques. It takes advantage of a specific modeling language to define data relationships while bypassing SQL. Looker is also tightly integrated with a great number of Google datasets and tools, including Google Analytics, as well as with several third-party data and business tools.

Looker earns good marks for reporting granularity, scheduling, and extensive integration options that create an open and governable ecosystem. Power BI tends to perform better than Looker in terms of breadth of service due to its ecosystem of Microsoft Power Platform tools; users also tend to prefer Power BI for a comprehensive suite of data tools that aren’t too difficult to learn how to use.

Because each tool represents such a different set of strengths, it’s a tie for this category.

Best for Ease of Use and Implementation: Power BI

In general, users who have tried out both tools find that Power BI is easier to use and set up than Looker.

Power BI provides users with a low-code/no-code interface as well as a drag-and-drop approach to its dashboards and reports. Additionally, its built-in AI assistance — which continues to expand with the rise of Copilot in Power BI — helps users initiate complex data analytics tasks regardless of their experience with this type of technology or analysis.

For some users, Looker has a steep learning curve because they must learn and use the LookML proprietary programming language to set up and manage their models in the system. This can be difficult for users with little experience with modeling languages, but many users note that the language is easy to use once they’ve learned its basics. They add that it streamlines the distribution of insights to staff across many business units, which makes it a particularly advantageous approach to data modeling if you’re willing to overcome the initial learning curve.

The conclusion: Power BI wins on general use cases for a non-technical audience whereas Looker wins with technical users who know its language.

Best for Advanced Data Analytics: Looker

While both tools offer unique differentiators for data analytics operations, Looker outperforms Power BI with more advanced, enterprise-level data governance, modeling, and analytics solutions that are well integrated with common data sources and tools.

Both tools offer extensive visualization options, but Looker’s data visualizations and reporting are more customizable and easier to configure to your organization’s specs and stakeholders’ expectations. Looker also streamlines integrations with third-party data tools like Slack, Segment, Redshift, Tableau, ThoughtSpot, and Snowflake, while also working well with Google data sources like Google Analytics. As far as its more advanced data analytics capabilities go, Looker surpasses Power BI and many other competitors with features like granular version control capabilities for reports, comprehensive sentiment analysis and text mining, and open and governed data modeling strategies.

However, Looker has limited support for certain types of analytics tasks, like cluster analysis, whereas Power BI is considered a top tool in this area. And, so far, Power BI does AI-supported analytics better, though Google does not appear to be too far behind on this front.

It’s a pretty close call, but because of its range of data analytics operations and the number of ways in which Google makes data analytics tasks customizable for its users, Looker wins in this category.

Also see: Best Data Analytics Tools 

Best for Integrations: It Depends

When it comes to integrations, either Power BI or Looker could claim the upper hand here.

It all depends on if you’re operating in a Microsoft shop or a Google shop. Current Microsoft users will likely prefer Power BI because of how well it integrates with Azure, Dynamics 365, Microsoft 365, and other Microsoft products. Similarly, users of Google Cloud Platform, Google Workspace, and other Google products are more likely to enjoy the integrated experience that Looker provides with these tools.

If your organization is not currently working with apps from either of these vendor ecosystems, it may be difficult to set up certain third-party integrations with Power BI or Looker. For example, connecting Power BI to a collaboration and communication tool like Slack generally requires users to use Microsoft Power Automate or an additional third-party integration tool. Looker’s native third-party integrations are also somewhat limited, though the platform does offer easy-to-setup integrations and actions for tools like Slack and Segment.

Because the quality of each tool’s integrations depends heavily on the other tools you’re already using, Power BI and Looker tie in this category.

Best for Pricing: Power BI

Power BI is consistently one of the most affordable BI solutions on the market. And while Looker Studio in particular helps to lower Looker’s costs, the platform is generally considered more expensive.

Power BI can be accessed through two main free versions: Power BI Desktop and a free account in Microsoft Fabric. The mobile app is also free and easy to access. But even for teams that require more functionality for their users, paid plans are not all that expensive. Power BI Pro costs only $10 per user per month, while Power BI Premium is $20 per user per month.

Looker, on the other hand, is more expensive, requiring users to pay a higher price for its enterprise-class features. The Standard edition’s pay-as-you-go plan costs $5,000 per month, while all other plans require an annual commitment and a conversation with sales to determine how much higher the costs will be.

Additionally, there are user licensing fees that start at $30 per month for a Viewer User; users are only able to make considerable changes in the platform as either a Standard User or a Developer User, which costs $60 and $125 per user per month respectively.

Power BI takes the lead when it comes to pricing and general affordability across its pricing packages.

Also see: Top Digital Transformation Companies

Why Shouldn’t You Use Looker or Power BI?

While Looker and Power BI are both favorites among data teams and citizen data scientists alike, each platform has unique strengths — and weaknesses — that may matter to your team. If any of the following qualities align with your organizational makeup, you may want to consider investing in a different data platform.

Who Shouldn’t Use Looker

The following types of users and companies should consider alternatives to Looker:

• Users who want an on-premises BI tool; most Looker features, including useful connections to Looker Studio, are only available to cloud users.
• Users who are not already working with other Google tools and applications may struggle to integrate Looker with their most-used applications.
• Users with limited computer-language-learning experience may struggle, as most operations are handled in Looker Modeling Language (LookML).
• Users who want a lower-cost BI tool that still offers extensive capabilities to multiple users.
• Users in small business settings may not receive all of the vendor support and affordable features they need to run this tool successfully; it is primarily designed for midsize and larger enterprises.

Who Shouldn’t Use Power BI

The following types of users and companies should consider alternatives to Power BI:

• Users who need more unique and configurable visualizations to represent their organization’s unique data scenarios.
• Users who are not already working with other Microsoft tools and applications may struggle to integrate Power BI into their existing tool stack.
• Users who consistently process and work with massive quantities of data; some user reviews indicate that the system gets buggy and slow with higher data amounts.
• Users who work with a large number of third-party data and business apps; Power BI works best with other Microsoft tools, especially those in the Power Platform.
• Users who consistently need to run more complex analytics, such as predictive analytics, may need to supplement Power BI with other tools to get the results they need.

If Looker or Power BI Isn’t Ideal for You, Check Out These Alternatives

Both Looker and Power BI offer extensive data platform features and capabilities, as well as smooth integrations with many users’ most important data sources and business applications. However, these tools may not be ideally suited to your team’s particular budget, skill sets, or requirements. If that’s the case, consider investing in one of these alternative data platform solutions:

Domo

Domo puts data to work for everyone so they can extend their data’s impact on the business. Underpinned by a secure data foundation, the platform’s cloud-native data experience makes data visible and actionable with user-friendly dashboards and apps. Domo is highly praised for its ability to help companies optimize critical business processes at scale and quickly.

Yellowfin

Yellowfin is a leading embedded analytics platform that offers intuitive self-service BI options. It is particularly successful at accelerating data discovery. Additionally, the platform allows anyone, from an experienced data analyst to a non-technical business user, to create reports in a governed way.

Wyn Enterprise

Wyn Enterprise offers a scalable embedded business intelligence platform without hidden costs. It provides BI reporting, interactive dashboards, alerts and notifications, localization, multitenancy, and white-labeling in a variety of internal and commercial apps. Built for self-service BI, Wyn offers extensive visual data exploration capabilities, creating a data-driven mindset for the everyday user. Wyn’s scalable, server-based licensing model allows room for your business to grow without user fees or limits on data size.

Zoho Analytics

Zoho Analytics is a top BI and data analytics platform that works particularly well for users who want self-service capabilities for data visualizations, reporting, and dashboarding. The platform is designed to work with a wide range of data formats and sources, and most significantly, it is well integrated with a Zoho software suite that includes tools for sales and marketing, HR, security and IT management, project management, and finance.

Sigma

Sigma is a cloud-native analytics platform that delivers real-time insights, interactive dashboards, and reports, so you can make data-driven decisions on the fly. With Sigma’s intuitive interface, you don’t need to be a data expert to dive into your data, as no coding or SQL is required to use this tool. Sigma has also recently brought forth Sigma AI features for early access preview.

Review Methodology

Looker and Power BI were reviewed based on a few core standards and categories for which data platforms are expected to perform. The four categories covered below have been weighted according to how important they are to user retention over time.

User experience – 30%

When it comes to user experience, we paid attention to how easy each tool is to use and implement and how many built-in support resources are available for users who have trouble getting started. Additionally, we considered how well the platform performs under certain pressures, like larger data loads, security and user control requirements, and more complex modeling and visualization scenarios. Finally, we considered the availability of the tool in different formats and how well the tool integrates with core business and data applications.

Scalability and advanced analytics compatibility – 30%

Our review also considered how well each platform scales to meet the needs of more sophisticated analytics operations and larger data processing projects. We paid close attention to how the platform performs as data loads grow in size and complexity, looking at whether user reviews mention any issues with lag times, bugs, or system crashes. We also considered what tools were available to assist with more complex analytics tasks, including AI-powered insights and support, advanced integrations and plugins, and customizable dashboards and reports.

Integrability – 20%

We considered how well each tool integrated with other software and cloud solutions from the same vendor as well as how easy it is to set up third-party integrations either via prebuilt connectors or capable APIs. In particular, we examined how well each platform integrated with common data sources outside of its vendor ecosystem, including platforms like Redshift, Snowflake, Salesforce, and Dropbox.

Cost and accessibility – 20%

For cost and accessibility, we not only focused on low-cost solutions but also on how well each solution’s entry-level solutions perform and meet user needs. We assessed the user features available at each pricing tier, how quickly pricing rises — especially for individual user licenses or any required add-ons, and whether or not a comprehensive free version was available to help users get started.

Bottom Line: Looker vs. Power BI

Microsoft’s Power BI has consistently been among the top two and three business intelligence tools on the market, recruiting and retaining new users with its balance of easy-to-use features, low costs, useful dashboards and visualizations, range of data preparation and management tools, AI assistance, and Microsoft-specific integrations. It is both a great starter and advanced data platform solution, as it offers the features necessary for citizen data scientists and more experienced data analysts to get the most out of their datasets.

Power BI tends to be the preferred tool of the two because of its general accessibility and approachability as a tool, but there are certain enterprise user needs for reporting and analytics distribution where Looker far outperforms Power BI. And for those heavily leaning on Google platforms or third-party applications, Looker offers distinct advantages to skilled analysts.

Ultimately, Looker doesn’t really try to compete head-to-head with Microsoft, because they each target different data niches and scenarios. It’s often the case that prospective buyers will quickly be able to identify which of these tools is the best fit for their needs, but if you’re still not sure, consider reaching out to both vendors to schedule a hands-on demo.

Read next: Best Data Mining Tools and Software

The post Looker vs. Power BI: 2024 Software Comparison appeared first on eWEEK.

In this guide, we’ll dissect AI policy and governance in greater detail, explaining how an AI policy that emphasizes all areas of AI governance can lead to more effective use of AI and a more ethical and successful business operation as a whole.

What Is An AI Policy?

An artificial intelligence policy is a dynamic, documented framework for AI governance that helps organizations set clear guidelines, rules, and principles for how AI technology should be used and developed within the organization.

Creating an AI policy should help your business leaders clarify and highlight any ethical, legal, or compliance standards to which your organization is committed, as well as identify the “who,” “what,” “when,” “why,” and “how” for strategic AI usage that aligns with overall organizational goals and strategies.

Every organization’s AI policy will look a little different to meet their specific objectives for AI governance, but in general, most AI policies include some version of the following components and structural elements:

• An overarching vision for AI usage and growth in the organization.
• Mission statements, clear objectives, and/or KPIs that align with this vision.
• Detailed information about regional, industry-specific, and relevant regulatory compliance laws as well as other ethical considerations.
• A catalog of approved tools and services that can be used for AI development and deployment purposes.
• Defined roles and responsibilities related to AI usage.
• An inventory and procedure for data privacy and security mechanisms.
• A defined procedure for reporting and addressing AI performance and security issues.
• Standards for AI model performance evaluation.

What Is AI Governance?

AI governance is the active management of best practices that includes policies, standardized processes, and data and infrastructure controls that contribute to a more ethical and controlled artificial intelligence ecosystem.

With effective and active AI governance, training data, algorithms, and model infrastructure can be more closely monitored and controlled throughout initial development, training and retraining, and deployment. AI governance contributes to a more efficient AI operation as well as compliance with relevant data privacy and AI ethics regulations.

Why Is AI Governance Important?

AI governance is one of the most effective ways to establish, organize, and enforce standards for AI development and use that encourages ethical and compliant practices, transparency, continual monitoring and improvement, and cross-team collaboration.

AI governance can improve AI model usage outcomes and help organizations use AI in a way that protects customer data and aligns with compliance requirements. Careful and active governance maintains a company’s reputation as an ethical operator, not only with their customers but also with their partners and the industry at large.

Establishing an independent AI governance timetable can also help your organization get more out of the AI technology you’re using, as enacting this type of plan requires your team to flesh out its AI vision, goals, and responsibilities in more granular detail. The accountability that gets built into ongoing AI governance helps to prevent and mitigate dangerous biases, create a plan of action for when AI development or use goes awry, and reemphasizes the importance of maintaining personal data privacy and security.

For a deeper understanding of governance in the enterprise, read our comprehensive overview, What is Data Governance.

Who Manages AI Governance?

AI governance is a complex process, especially if your organization is working with multiple generative AI models or other large-scale AI platforms. The following individuals and teams play important roles in different aspects of AI governance and management:

• Executive Leaders: An organization’s C-suite and other top leaders should establish the overall direction, goals, and vision for AI governance and associated AI policies. Regardless of their specific title, all business executives should be clear on what AI tools are being used and what regulations and policies are in place to regulate that usage.
• Chief Information Officer: Unless your organization prefers to have a Chief AI Officer or Chief Technical Officer oversee this kind of work, the CIO is the primary business leader who takes broader organizational strategies and goals and applies them to actual AI governance development and implementation. This individual is also responsible for ensuring that AI integrates smoothly and securely with all other technologies and infrastructures in your business’s tech stack.
• Chief Data Officer: The CDO is primarily responsible for data governance and data-level quality assurance. In their role, they work to manage data quality, data privacy and compliance, and transparent data preparation workflows for AI model training sets.
• Chief Compliance Officer and Legal/Compliance Teams: This individual or group of individuals keeps up with international, national, regional, industry-specific, and other regulations that may impact how your organization can use data — including PII and intellectual property — and AI models. If a Chief Ethics Officer works among this team, this work may go beyond simple compliance management and move toward setting up ethical decision-making and training frameworks.
• Data Science, AI, and IT Security Teams: These are the teams that handle the hands-on development tasks for training data, algorithms, models, performance monitoring, and security safeguards. While they may not have a hand in setting AI governance standards, they will likely play the biggest role in carrying out these standards.
• AI Ethics Committee: If your organization has established an AI ethics committee that operates separately from your C-suite executives, these committee members will act as advisors to leadership to establish – and actively maintain – governance frameworks that consider AI ethics from all angles, including personal privacy, transparent data sourcing and training, and environmental impact.
• HR and Learning and Development Teams: These leaders are in charge of incorporating AI governance best practices and rules into the recruitment and hiring process so all new members of the team are aware of the roles and responsibilities they have when using AI. This team may not come up with the actual training materials or goals, but because of their background with other types of training, they may be tasked with leading AI usage training across the organization.
• Third-Party Consultants: If your organization chooses to hire third-party consultants for data management, AI development, or strategic planning, these individuals may take over some or all of the other tasks covered above. However, you’ll want to make sure key stakeholders in your organization work collaboratively with these advisors to create an AI governance policy that is both comprehensive and fitted to your specific needs.
• Government and Industry Regulators: Depending on the industry or region you’re working in, third-party regulators could play a major role in determining what AI governance looks like for your organization, as they establish and enforce rules for ethical AI and data use. Many countries and regional groups like the EU are currently working on more comprehensive AI legislation, so expect this group’s role in AI governance to grow quickly in the coming months and years.

The Benefits of Having an AI Policy for AI Governance

An AI policy extends several benefits to organizations that are looking to develop a more comprehensive AI governance strategy. These are just a handful of the ways in which a dedicated policy can help you stay on task, compliant, and oriented with your initial vision:

• Structured guidance for all AI tool developers and users: This type of AI policy can act as a user manual for both AI developers and users of these tools, as it considers the entire AI lifecycle, from development to deployment to ongoing monitoring and fine-tuning. The standardized rules that are part of this type of policy facilitate cross-organizational buy-in and help your technical teams create a roadmap for AI best practices in real-world scenarios.
• A mechanism for widespread accountability: AI policies provide documented rules for organizational and role-specific AI best practices. This means that all relevant stakeholders have a point of reference that clearly outlines their roles, responsibilities, procedures, limitations, and prohibitions for AI usage, which helps to avoid both ethical and compliance issues.
• Better adherence to regulatory and data security laws: While the leaders in your organization are likely aware of regulatory and data security laws and how they apply to your business, chances are most other employees could benefit from additional clarification. Enforcing an AI policy that reiterates these laws and how they apply to your organization can assist your compliance and legal teams in communicating and mitigating issues with compliance laws at all levels of the organization.
• Clear outline of data privacy standards and mechanisms: Beyond simply stating data security and compliance expectations, AI policies detail how data privacy works and what mechanisms are in place to protect data when it’s handled, stored, and processed for AI models. This level of detail guides all employees in how they should protect an organization’s most sensitive data assets and also gives the business a clear blueprint for what they should look for and where they should look during AI audits.
• Builds customer trust and brand reputation: As AI’s capabilities and use cases continue to expand, many people are excited about the possibilities while others are more distrusting of the technology. Establishing an AI policy that enforces AI governance while creating more transparency and explainability is a responsible way to move forward and gives your customers more confidence in how your organization uses AI in its operations.
• Preparation for incoming AI regulations: While few AI-specific regulations have passed into law at this point, several groups, including the EU, the U.K., and the U.S., are working toward more comprehensive AI regulations and laws. Creating a comprehensive AI policy now can help your organization proactively align with AI best practices before they are required in your regions of operation.

AI Policy and Governance Best Practices

If your AI policy is not clear on its expectations for AI governance and general use, your teams may run into issues with noncompliance, security, and other avoidable user errors. Follow these best practices to help every member of your team, regardless of how they work with AI, remain committed to high standards of AI governance:

• Pay attention to relevant regulations: Consider important regional, national, and industry-specific regulations and stay up-to-date with your knowledge so AI systems remain in compliance at all times.
• Implement standards for data security and data management: AI is a data-driven technology, so be sure to use appropriate data management tools, strategies, and processes to protect and optimize that asset.
• Cover the entire AI lifecycle in your AI policy: Your AI policy should not simply focus on how AI models are developed or how they are used. Instead, create a comprehensive policy that covers everything from data preparation and training to model creation and development, model deployment, model monitoring, and model fine-tuning.
• Establish ethical use standards and requirements: Keep in mind employee-specific roles and responsibilities and set up role-based access controls or other security standards to underpin those rules and protect your consumers’ most sensitive data. Additionally, pay attention to important concepts like AI bias, fairness, data sourcing methods, and other factors that impact ethical use.
• Create standards for ongoing evaluation of model performance: What will you be looking at when you’re monitoring your models in “the real world”? Your AI policy should detail important performance metrics and KPIs so you can stick to your goals and fairly evaluate performance at all stages of AI usage and development.
• Accompany your AI policy with dedicated user training: To help all employees understand how your AI governance policy applies to their work, provide dedicated user training that covers cybersecurity, ethical use, and other best practices, ideally with real-world scenarios and examples.
• Document and regularly update your AI policies: AI policies should not be static documents; they should dynamically change as tooling, user expectations, industry trends and regulations, and other factors shift over time.
• Communicate your ethical practices to relevant stakeholders and customers: Strategically and transparently communicate your governance standards and details of your policy to third-party investors, customers, partners, and other important stakeholders. This communication strategy helps to establish additional trust in your brand and its ethical approach to AI.

Bottom Line: Using Your AI Policy and Governance Best Practices for Better AI Outcomes

Most businesses are already using AI in some fashion, or will likely will adopt the technology soon to keep up with the competition in their industry. Creating and adhering to an AI policy that covers compliance, ethics, security, and practical use cases in detail not only supports these organizations when getting started with large-scale AI projects but also helps them meet customer and legal expectations when using AI technology.

Developing detailed AI policies and governance strategies often feels like an overwhelming process, and especially for organizations that are just dipping their toes into the AI pool, establishing these guidelines may feel daunting. But this is the wrong way to look at it; instead, think of your AI governance policy as an insurance policy for the modern enterprise. Especially as AI regulations become more well-defined in the coming months and years, it will pay to have an AI policy that proactively paves the way to more responsible and effective artificial intelligence.

For more information about sensitive issues involving AI policy and governance, read our guide, AI and Privacy Issues: What You Need to Know.

The post AI Policy and Governance: What You Need to Know appeared first on eWEEK.

As advanced technologies like artificial intelligence come to the fore, many customers are expecting faster and higher-quality outputs from the brands they trust. In response, enterprises are increasingly looking for ways to automate their business processes – hence the rise of intelligent automation.

In this guide, we’ll take a closer look at what intelligent automation is, how it works, the benefits that come with using it, and how it can be applied to common business processes and automation scenarios most effectively.

What Is Intelligent Automation?

Intelligent automation is an advanced automation process that merges artificial intelligence and machine learning with robotic process automation to automate business process workflows and create intelligent, robotic agents that can take over some of an organization’s workflow-based tasks.

Robotic process automation bots alone can handle a number of automated business tasks. But they don’t possess the additional human-like capabilities to go beyond routine training and take on new tasks that require cognitive and sensory capabilities.

When combined with RPA, artificial intelligence and machine learning give bots the algorithmic knowledge to comprehend and execute automated tasks at a deeper level. The training data involved in IA is typically a large set of data from various sources and in diverse formats: both structured and unstructured.

In essence, this sophisticated AI training gives RPA-powered machines the capacity for decision intelligence, or at least the context to make data-driven decisions that are largely independent from regular human intervention.

But for bots to move beyond simple and routine task automations, they often require more than basic AI and ML algorithmic training. For example, many upper level bots are trained with deep learning, neural networks, and natural language processing so they can understand human language and generate unique content on a range of topics.

To give IA machines the ability to “see” or interact with their surroundings, many of these bots also receive training based on computer vision and optical character recognition (OCR). With this training in particular, intelligently automated machines can take on tasks in retail, manufacturing, and other settings that typically require a pair of eyes and sensory skills.

Intelligent Automation vs. Robotic Process Automation

Robotic process automation is one component of intelligent automation. When you work strictly with RPA, you can train bots to handle simpler tasks on a routine schedule. Despite this simplicity, these RPA bot can be remarkably productive – in many cases they can handle a large portion of a staff member’s routine tasks.

However, to accomplish higher-level tasks, you need the deeper contextual and cognitive capabilities that come with artificial intelligence. Intelligent automation combines the best of both AI and RPA technologies to meet its automation goals. The challenge, of course, is that developing these bots requires more investment, and in some cases may also require tech support that has advanced training.

Intelligent Automation vs. Hyperautomation

Hyperautomation is more focused on transforming overall business strategies to wholly incorporate smart automations across infrastructure, departments, and projects. It is more all-encompassing (though the technology remains nascent) and has a greater capacity to fully transform a workplace.

Intelligent automation technologies can be used to achieve hyperautomation goals, but hyperautomation itself typically involves more organization-wide strategy and planning to get up and running. It necessarily requires a longer period to fully implement, and also needs a trained support staff to maintain and adjust each of the many elements as workflow progresses.

Business Use Cases for Intelligent Automation

Intelligent automation can be incorporated into a range of business use cases and industries. With the right training and monitoring in place, many organizations are beginning to bring IA into their workflows in the following ways:

• Customer service and contact center agents: Some organizations are creating more sophisticated robotic call center agents to handle calls and chats without sounding so scripted; IA tools may also be used to more efficiently manage call logs, score leads, personalize marketing campaigns, and make recommendations based on buyer history.
• Smart manufacturing and supply chain management: IA-powered robots can take on human tasks — or even chains of tasks — on factory production floors and make adjustments to their performance based on real-time training and feedback. They can also use applied predictive analytics and computer vision/machine vision to manage quality and maintenance schedules for both factory machines and manufactured products, while also considering how these changes impact supply chain schedules and logistics.
• DevOps: Intelligent automation is particularly effective for automating software testing and recommendations and actions for CI/CD. It can also be used to manage cybersecurity efforts in DevSecOps scenarios.
• Cybersecurity management: IA bots can handle the full cybersecurity management lifecycle, not only detecting vulnerabilities and issues on a massive scale but using predictive analytics and smart recommendations to actually make the necessary improvements and handle threat response activities themselves.
• Insurance: In complex and tedious insurance workflows, like claims and risk management, IA bots can comb through large amounts of data quickly and automate tasks like claim intake and settlement. When these tasks are automated at scale, it can increase insurance company productivity and reduce the chance of risky or erroneous claims.
• Human resources and recruitment support: Certain key aspects of recruitment and HR can be automated with IA agents, including onboarding and payroll processing tasks.
• Healthcare: IA in healthcare can handle some of the back-office administrative tasks of a healthcare facility, following automated workflows while adhering to cybersecurity and compliance requirements for data processing. IA has also been used to manage large-scale tasks in public health, like COVID-19 vaccination distribution and tracking.
• Consumer self-service technologies: While many of these technologies are still fairly new, self-driving cars, smart checkout kiosks, and similar self-service technologies are made smarter with the help of IA.

More on a similar topic: Generative AI: 15 Enterprise Use Cases You Can Implement

Benefits of Intelligent Automation

Intelligent automation delivers a range of benefits not only to the business leaders enacting the strategy but also to the workers interacting with the technology and the customers on the receiving end of production.

These are some of the most common benefits that come from bringing intelligent automation into business workflows:

• Optimized productivity: IA goes beyond simple automation and focuses on larger and more impactful automation scenarios. This technology supports faster and higher quality product and service delivery, which benefits the business and gives customers a better experience overall.
• Avoiding error-prone human task work: Because intelligent automation can tackle more complex workflows and processes than traditional automation can, IA is one of the most effective ways for organizations to automate task work that human workers most frequently make mistakes on.
• Affordable and scalable process automation: If you have the resources and commitment to scaling your process automations, intelligent automation can easily be scaled to meet your new requirements. While additional compute or processing resources may be necessary as you scale, you likely won’t have to invest in entirely new technologies or infrastructure to grow your IA footprint.
• Compatible with various industries and technologies: Intelligent automation is flexible enough to work across industries, sectors, and project types because of its algorithmic training and sensory depths. IA technology is also often integrable with an organization’s other automation and process management technologies, including CRMS and ERPs.
• Backfilling in environments with worker shortages: Although intelligently automated machines are not currently equipped to take on all human tasks, their training is sophisticated enough so that they can truly backfill human roles in a variety of project and workplace settings and scenarios. This is especially helpful for organizations that are having trouble filling certain roles on their teams.
• A more unified operational model: Intelligent automation is designed to work with modern cloud and AI technologies but also legacy and hybrid technologies; it provides a way to intelligently integrate best practices and tools across this stack, which can facilitate more unified technical operations and strategy for the organization.

Best Practices for Getting Started With Intelligent Automation

Intelligent automation is a complex and multifaceted automation strategy that requires buy-in, dedicated training and change management, thoughtful planning, and ongoing strategic pivots.

To get the most out of your intelligent automation initiatives, follow these best practices for getting started:

Involve All Relevant Company Stakeholders

Data scientists, automation engineers, and other IT team players should be involved from the start in customizing IA to fit the organization, but other business leaders and stakeholders should also be involved when intelligent automations are first being discussed to ensure the technology meets organization-wide demands and gets buy-in from all departments and project teams.

Set Goals and Consider Your Most Important Use Cases

At this stage, seek out employee feedback on tedious task work that could be automated or otherwise handed off; don’t simply ask managers, but be willing to talk to employees who are in the weeds of the organization’s most tedious tasks.

Additionally, consider your budget and any tools or resources you may still need to get started, as well as any measurable goals or outcomes you hope to achieve with intelligent automation.

Invest in Flexible, Integrable IA Tools

A long list of AI and RPA tools are on the market today, but not all of them effectively combine the strengths of both technology types to achieve intelligent automation. Research the options that are available on the market, paying particularly close attention to any advanced technologies and features that meet your needs. Also, pay close attention to how — or if — these platforms will integrate with your other business process management tools.

Test and Monitor Automations At All Stages of Development and Deployment

At all stages of intelligent automation, test how automations are performing and if they are meeting their intended purpose. It’s especially important to quality-test automations that affect customer-facing interactions, such as intelligent customer service agents or autonomous devices.

QA specialists or automation engineers on your team are likely the best fit to test how automations are performing, and different types of automation testing and monitoring tools can supplement their work.

Follow AI Ethics and Ethical Best Practices

Because intelligent automation is so heavily entwined with artificial intelligence, it’s important to consider the ethical implications of the data you’re using and where and how you apply artificial intelligence in your workflows. Ensure all of your most sensitive data — particularly PHI and PII — is securely stored separately from these technologies, and frequently audit your IA tools and results to ensure data is being used ethically.

If the tools you’re using aren’t transparent enough to give you this kind of visibility, consider switching up your toolset or strategy to create more visibility. Taking this step will help you to protect your consumers’ data as well as any other sensitive business data from unauthorized access and usage.

Learn more: Generative AI Ethics: Concerns and Solutions

Bottom Line: Intelligent Automation in Modern Enterprise Workflows

Intelligent automation has gained steam in recent years, not only because it offers exciting prospects for modern business productivity but also because it’s actually possible now.

Advanced technologies like generative AI and computer vision are becoming more accessible and optimized for the everyday user. Most enterprises already use some kind of automation technology in their daily work and are familiar with the organizational change management, training, and ongoing commitment that comes with automating business tasks.

In short, this is a dynamic time for intelligent automation and a great time to get started with the technology. For the best possible results, follow the best practices listed above and don’t lose sight of the people who need to be involved. Especially as this technology and its capabilities evolve, you’ll want to ensure that all relevant stakeholders in your business receive the upskilling training they need to keep up with the technology and take on new and more challenging tasks that are beyond the robots’ purview – for now.

Read next: 10 Best Machine Learning Platforms

The post Overview of Intelligent Automation appeared first on eWEEK.

Generally considered a subset of artificial intelligence (AI), machine learning systems generate algorithms based on training datasets and then deliver relevant outputs, often without expressly being programmed to produce the exact outcomes they drive.

The autonomous learning capabilities of AI and ML platforms are at the center of today’s enterprises. The technology is increasingly being used to make important decisions and drive automations that improve enterprise operations across disciplines. In recent years, ML technology has also formed the foundation for generative AI models, which are trained to generate new content through larger datasets and more complex ML algorithms.

With its range of relevant business use cases in the modern enterprise, machine learning platform technology has quickly grown in popularity, and vendors have expanded these platforms, capabilities and offerings to meet growing demands.

In this guide, we cover 10 of the best machine learning platforms on the market today, detailing their specific features, pros and cons, and any areas where they particularly stand out from the competition.

Best Machine Learning Software: Comparison Chart

Top 10 Machine Learning Software Platforms

Alteryx Machine Learning: Best for Citizen Data Scientists and Developers

Alteryx has emerged as a leader in the machine learning space for tackling extremely complex machine learning projects through an accessible interface. The drag-and-drop platform incorporates highly automated ML features for both experienced data scientists and less technical business users. Many users particularly praise this platform for its built-in Education Mode, which makes the no-code platform even easier to learn and adjust to your particular use cases.

The platform connects to an array of open-source GitHub libraries — including Woodwork, Compose, Featuretools, and EvalML — and handles numerous data formats and sources. Alteryx also offers powerful visualization tools and feature engineering tools as well as a large and active user community.

Pricing

Pricing information for Alteryx Machine Learning is only available upon request. Prospective buyers can contact Alteryx directly for more information and/or get started with the product’s free trial on either desktop or cloud.

Key Features

• Automated machine learning and feature engineering.
• Automated insight generation for data relationships.
• Built-in Education Mode for learning and optimizing ML development.
• Access to open-source packages and libraries in GitHub.
• No-code, cloud-based format.

Pros

• Offers strong data prep and integration tools along with a robust set of curated algorithms.
• Excellent interface and powerful automation features.

Cons

• Macros and APIs for connecting to various data sources can be difficult to set up and use.
• Some users complain about slow load and processing speeds.

Databricks Data Intelligence Platform: Best for Enterprise-Scale Data Management and Feature Engineering

The Databricks Data Intelligence Platform offers a centralized environment with powerful tools and features that facilitate machine learning and the data preparation work that goes into successful ML model developments.

Managed MLflow is one standout feature that relies on an open-source platform developed by Databricks to manage complex interactions across the ML lifecycle. This platform is particularly useful for organizations that want a combination of self-service and guided data management and feature engineering capabilities that work for data from disparate sources and in different formats.

Interested users can take advantage of the platform for data processing and preparation — including for generative AI and large language models — and to prepare data production pipelines. They can also register and manage models through the Model Registry feature. In addition, the platform provides users with collaborative notebooks, the Feature Registry, and the Feature Provider, all of which support feature engineering requirements and MLOps with a strong, big-data-driven backbone.

Pricing

The Databricks platform is available at no base cost; instead, interested users will sign up and then pay for the features and quantities they use on a per-second basis. Users with larger usage requirements may be eligible for committed use discounts, which work across cloud environments. If you have inconsistent or smaller usage requirements, you’ll need to pay per product and per Databricks Unit (DBU) used:

• Workflows & Streaming Jobs: Starting at $0.07 per DBU.
• Workflows & Streaming Delta Live Tables: Starting at $0.20 per DBU.
• Data Warehousing Databricks SQL: Starting at $0.22 per DBU.
• Data Science & Machine Learning All Purpose Compute for Interactive Workloads: Starting at $0.40 per DBU.
• Data Science & Machine Learning Serverless Real-Time Inference: Starting at $0.07 per DBU.
• Databricks Platform & Add-Ons: Information available upon request.

A 14-day free trial is also available with limited features.

Key Features

• Open lakehouse architecture.
• REST-API-driven model deployment.
• Pretrained and fine-tuned LLM integration options.
• Self-service data pipelines.
• Managed MLflow with experiment tracking and versioning.

Pros

• The open data lakehouse format makes it easier to work with data from different sources and for different use cases; users appreciate that the platform can scale for data orchestration, data warehousing, advanced analytics, and data preparation for ML, even for larger datasets.
• This is a highly scalable environment with excellent performance in a framework that users generally find easy to use; many features are built on open-source data technologies.

Cons

• Can be pricey, especially when compared to completely free and open-source solutions in this space.
• Some visualization features are limited and difficult to set up.

Dataiku: Best for Extensibility

Dataiku is a popular, user-friendly ML platform that delivers all the tools required to build robust ML models, including strong data preparation features. An AutoML feature is another great component of the tool that is designed to fill in missing values and seamlessly convert non-numerical data into numerical values. Its data preparation, visualization, and feature engineering capabilities are well-reviewed components of the platform, but where Dataiku really sets itself apart is its extensibility and range of integrations.

Users can easily integrate many of today’s top generative AI services and platforms, including from OpenAI, Cohere, Anthropic, and Hugging Face. A range of public and proprietary plugins are available through GUI-based code packages, and integrations are also available with leading DevOps and data science visualization frameworks. Dataiku also supports custom modeling using Python, R, Scala, Julia, Pyspark, and other languages.

Pricing

Four plan options are available for Dataiku users. Pricing information is not provided for the paid plans, though other details about what each plan covers are included on the pricing page. A 14-day free trial is also available for each of the paid plans listed below:

• Free Edition: $0 for up to three users and installation on your personal infrastructure. Other limited features are included.
• Discover: A paid plan for up to five users that includes more than 20 database connectors, Spark-based data processing, and limited automations. Pricing information is available upon request.
• Business: A paid plan for up to 20 users that includes unlimited Kubernetes-based computations, full automation, and advanced security features. Pricing information is available upon request.
• Enterprise: A paid plan that includes all database connectors, full deployment capabilities, an isolation framework, and unlimited instances and resource governance. Pricing information is available upon request.

Key Features

• Feature store and automatic feature generation.
• Generative AI platform integrations.
• White-box explainability for ML model development.
• Prompt Studios for prompt-based LLM model development.
• Public and proprietary plugins for custom visual recipes, connectors, processors, and more.

Pros

• Dataiku is among the most flexible machine learning platforms, and it delivers strong training features.
• Dataiku easily integrates and extends its functionalities with third-party DevOps, data science visualization, and generative AI tools, frameworks, and services.

Cons

• Dataiku has a somewhat unconventional development process that can slow down model development.
• Especially as the tool updates, some users have experienced difficulties with outages.

Also see: Best Data Analytics Tools

Vertex AI: Best for Model Organization and Management

The Vertex AI platform is a leading cloud-based AI and ML solution that taps into the power of Google Cloud to deliver a complete set of tools and technologies for building, deploying, and scaling ML models. It supports pre-trained custom tooling, AutoML APIs that speed up model development, and a low-code framework that typically results in 80% fewer lines of code.

It’s also a highly organized platform that gives users accessible tools to manage their models at all stages of development. For example, the Vertex AI Model Registry is available for users who want a central repository where they can import their own models, create new models, classify models as ready for production, deploy models to an endpoint, evaluate models, and look at ML models both at a granular level and in an overview format. Additionally, Vertex AI supports nearly all open-source frameworks, including TensorFlow, PyTorch, and scikit-learn.

Pricing

Pricing for Vertex AI is highly modular and based on the tools and services, compute, and storage you use, as well as any other Google Cloud resources you use for ML projects. We’ll cover the estimates for some of the most commonly used features below, but it’s a good idea to use the pricing calculator or contact Google directly for a custom quote that fits your particular needs:

• Generative AI (Imagen model for image generation): Starting at $0.0001.
• Generative AI (Text, chat, and code generation): Starting at $0.0001 per 1,000 characters.
• AutoML Models (Image data training, deployment, and prediction): Starting at $1.375 per node hour.
• AutoML Models (Video data training and prediction): Starting at $0.462 per node hour.
• AutoML Models (Text data upload, training, deployment, prediction): Starting at $0.05 per hour.
• Vertex AI Pipelines: Starting at $0.03 per pipeline run.

A free trial is available for Vertex AI as well, though only as part of a greater free trial for all of Google Cloud. The Google Cloud free trial gives all users $300 in free credits to test out the platform.

Key Features

• Model Garden library with models that can be customized and fine-tuned.
• Native MLOps tools, including Vertex AI Evaluation, Vertex AI Pipelines, and Feature Store.
• Custom ML model training workflows.
• Vertex AI prediction service with custom prediction routines and prebuilt containers.
• Vertex AI Model Registry for production-ready model deployment.

Pros

• Despite powerful ML capabilities, the platform is fairly user-friendly, relatively easy to use, and highly scalable.
• It delivers strong integrations with other Google solutions, including BigQuery and Dataflow.

Cons

• Vertex AI is not as flexible and as customizable as other ML platforms. It also lacks support for custom algorithms.
• Some users complain about the high price and limited support for languages beyond Python.

H2O-3: Best for R and Python Programmers

H2O-3 is the latest iteration of the open-source data science platform that supports numerous areas of AI, including machine learning. The platform is designed with numerous automation features, including feature selection, feature engineering, hyperparameter autotuning, model ensembling, label assignment, model documentation, and machine learning interpretability (MLI).

H2O-3 offers powerful features specifically designed for Natural Language Processing (NLP) and computer vision. R and Python programmers particularly appreciate this platform for its wide-ranging community support and easy download options that are compatible with the two languages.

Pricing

H2O-3 is a free and open-source solution that users can download directly from the vendor site or in AWS, Microsoft Azure, or Google Cloud.

Key Features

• Open-source, distributed, in-memory format.
• Support for gradient-boosted machines, generalized linear models, and deep learning models.
• AutoML-driven leaderboard for model algorithms and hyperparameters.
• Algorithms include Random Forest, GLM, GBM, XGBoost, GLRM, and Word2Vec.
• H2O Flow for no-code interface option; code-based options include R and Python.

Pros

• Excellent support for open-source tools, components, and technologies.
• Offers powerful bias detection and model scoring features.

Cons

• Some users complain about missing analysis tools and limited algorithm support.
• Overall performance and customer support lag behind competitors.

KNIME Analytics Platform: Best for Community-Driven ML Development

The KNIME Analytics Platform promotes an end-to-end data science framework designed for both technical and business users. This includes a comprehensive set of automation tools for tackling machine learning and deep learning. The KNIME platform delivers a low-code/no-code visual programming framework for building and managing models.

The platform includes a robust set of data integration tools, filters, and reusable components that can be shared within a highly collaborative framework. Speaking of collaboration, the KNIME community is one of the most active and collaborative open-source communities in this space. Users can additionally benefit from KNIME Community Hub, a separate software solution that allows users to collaborate with data science and business users from other organizations and review other users’ samples with few overhead limitations.

Pricing

KNIME is a free and open-source solution, though interested users may want to contact the vendor directly to determine if their particular use case will incur additional costs. The KNIME Analytics Platform can be freely downloaded on Windows, Mac, and Linux.

Key Features

• Open-source, low-code/no-code tooling.
• Drag-and-drop analytic workflows.
• Access to ML libraries like TensorFlow, Keras, and H2O.
• Workflow-building node repository and workflow editor.
• AutoML for automated binary and multiclass classification and supervised ML training.

Pros

• Provides an intuitive, low-code/no-code interface that makes it easy for non-data scientists and new users to build ML models.
• Delivers strong automation capabilities across the spectrum of ML tasks.

Cons

• Code-based scripting requirements through Python and R can introduce challenges for certain types of customizations.
• Some users complain that the platform is prone to consume excessive computational resources.

Also see: Top Data Mining Tools

MATLAB: Best for Supportive ML Apps and Trainings

MathWorks MATLAB is popular among engineers, data scientists, and others looking to construct sophisticated machine learning models. It includes point-and-click apps for training and comparing models, advanced signal processing and feature extraction techniques, and AutoML, which supports feature selection, model selection, and hyperparameter tuning.

MATLAB works with popular classification, regression, and clustering algorithms for supervised and unsupervised learning. And, despite its many complex features and capabilities, it is a relatively accessible tool that offers a range of detailed training and documentation to users, as well as accessible and easy-to-incorporate apps.

Pricing

MATLAB can be used by organizations and individuals of all different backgrounds and is sometimes used in combination with Simulink, a MATLAB-based environment for multidomain model programming. Multiple subscription options are available:

• Standard: $940 per year, or $2,350 for a perpetual license.
• MATLAB and Simulink Startup Suite: $3,800 per year.
• Academic: $275 per year, or $550 for a perpetual license.
• MATLAB and Simulink Student Suite: $99 for a perpetual license.
• Home/personal use: $149 for a perpetual license.

A 30-day free trial option is available for MATLAB, Simulink, and several other products.

Key Features

• Prebuilt MATLAB apps and toolboxes.
• Live Editor for scripting.
• Simulink for model-based design.
• Classification Learner App for data classification and training.
• Onramp, interactive examples, tutorials, and e-books for getting started with machine learning.

Pros

• The platform offers an array of powerful tools and capabilities within a straightforward user interface that is particularly friendly to advanced mathematical, research, and data science use cases.
• Extremely flexible, with excellent collaboration features, app integration opportunities, and scalability.

Cons

• Relies on a somewhat proprietary approach to machine learning. Lacks support for some open-source components and languages, which can also make the tool more expensive than other players in this space.
• Can be difficult to use for business constituents and other non-data scientists to get started, though the platform comes with extensive training options to bridge that gap.

Azure Machine Learning: Best for LLM Development

Automation is at the center of Azure Machine Learning. The low-code platform boasts 70% fewer steps for model training and 90% fewer lines of code for pipelines. It also includes powerful data preparation tools and data labeling capabilities, along with collaborative notebooks, which makes it a great one-stop shop for MLOps requirements.

As modern use cases for machine learning have drifted more and more toward generative AI, Azure Machine Learning has proven itself a leader in this type of ML model development. Users can track and optimize training prompts with prompt flow, improve outcomes with the Responsible AI dashboard, benefit from scalable GPU infrastructure, and work within a wide range of tools and frameworks.

Pricing

Similar to many other platforms in this space, Azure Machine Learning itself comes at no cost, but users will quickly rack up costs based on the compute and other Azure services they use. Pricing is highly variable for this tool, so we’ve only included estimates and starting prices for a few key compute options; prospective buyers should contact Microsoft directly for additional pricing information beyond what we’ve included here:

• D2-64 v3: Individual components range from $0 per hour to $2.67 per hour, depending on vCPUs, RAM, Linux VM, service surcharges, and annual savings plans selected.  For this option and the ones below, many of these costs will be stacked on top of each other, depending on which instance you select.
• D2s-64s v3: Individual components range from $0 per hour to $3.072 per hour, depending on vCPUs, RAM, Linux VM, service surcharges, and annual savings plans selected.
• E2-64 v3: Individual components range from $0 per hour to $1.008 per hour, depending on vCPUs, RAM, Linux VM, service surcharges, and annual savings plans selected.
• M-series: Individual components range from $0 per hour to $26.688 per hour, depending on vCPUs, RAM, Linux VM, service surcharges, and annual savings plans selected.
• H-series: Individual components range from $0 per hour to $2.664 per hour, depending on vCPUs, RAM, Linux VM, service surcharges, and annual savings plans selected.

Discounted prices may be available for stable and predictable workloads through Azure Reserved Virtual Machine Instances. A free trial of Azure is also available.

Key Features

• Open-source library and framework interoperability.
• Responsible AI framework and dashboard.
• Prompt flow for AI workflow orchestration, including for LLMs.
• Data preparation and labeling.
• Drag-and-drop designer with notebooks, automated machine learning, and experiments.
• Managed endpoints for model deployment and scoring.

Pros

• The drag-and-drop interface and low-code framework simplify ML model building.
• Extensive LLM development and optimization features are available; the platform also benefits from Microsoft’s deep investment in generative AI and OpenAI in particular.

Cons

• The pricing structure is difficult to understand and can quickly get expensive
• Some users complain about subpar documentation and difficulties with support.

RapidMiner: Best for Cross-Disciplinary Teams

RapidMiner is an ML platform vendor that promotes the idea of “intuitive machine learning for all” through both code-based ML and visual low-code tools that non-technical team members can learn how to use. The platform includes prebuilt templates for common use cases, as well as guided modeling capabilities. It also provides robust tools for validating and retesting models.

RapidMiner focuses on MLOps and automated data science through several key functions, including an auto engineering feature and automatic process explanations. It is a highly collaborative platform with a project-based framework, co-editing capabilities, and built-in user authentication and access control features.

Pricing

A free version of RapidMiner, called RapidMiner Studio Free, is available for desktop users who require no more than 10,000 data rows and one logical processor. The enterprise version of the platform is a paid subscription; prospective buyers will need to contact RapidMiner directly for specific pricing information. All users can benefit from a 30-day free trial of the full platform, and discounts are available for certain groups, including academics.

Key Features

• Codeless model ops.
• Accurate and finance-based model scoring.
• Built-in drift prevention.
• Native dashboards and reports and integrations with BI platforms.
• User-level choice between code-based, visual, and automated model creation with logging for all options.

Pros

• A strong focus on administrative controls for governance, reporting, and user access.
• Offers intuitive, low-code/no-code tools for non-data scientists as well as sophisticated code-based tools for data scientists.

Cons

• Some users complain about the heavy computational resource requirements involved with using RapidMiner.
• Can be crash-prone in certain situations and scenarios.

TensorFlow: Best for MLOps

TensorFlow is an open-source machine learning software library that extends itself beyond this primary role to support end-to-end machine learning platform requirements. It works well for basic ML model development but also has the resources and capacity to support more complex model developments, including for neural networks and deep learning models.

Although TensorFlow rarely labels itself as an MLOps platform, it offers all of the open-source flexibility, extensibility, and full-lifecycle capabilities MLOps teams need to prepare their data, build models, and deploy and monitor models on an ongoing basis. TensorFlow Extended (TFX) is a particularly effective version of the tool for creating scalable ML pipelines, training and analyzing models, and deploying models in a production-ready environment.

Pricing

TensorFlow is a free and open-source tool, though additional costs may be incurred, depending on other tools you choose to integrate with the platform. The tool can be deployed directly on the web, on servers, or on mobile or edge devices.

Key Features

• Pretrained models in the model garden and TensorFlow Hub.
• On-premises, mobile-device, browser, and cloud-based deployment options.
• Simple ML add-on for Google Sheets model training and evaluation.
• Production-ready ML pipelines.
• Data preparation and responsible AI tools to eliminate data bias.

Pros

• Many other platforms, including those on this list, are compatible with TensorFlow and its software library.
• TensorFlow is known for its helpful and active user community.

Cons

• The models you can build within TensorFlow are mostly static, which may not be the most agile option.
• Many users have commented on how it’s more difficult to use and understand than most other Python-based software libraries.

Also see: Real-Time Data Management Trends

Key Features of Machine Learning Software

While the goal is typically the same — solving difficult computing problems — machine learning software varies greatly. It’s important to review vendors and platforms thoroughly and understand how different features and tools work. The following key features are some of the most important to consider when selecting machine learning software:

Data Processing and Ingestion

It’s important to understand how the software ingests data, what data formats it supports, and whether it can handle tasks such as data partitioning in an automated way. Some packages offer a wealth of templates and connectors, while others do not.

Support for Feature Engineering

Feature engineering is crucial for manipulating data and building viable algorithms. The embedded intelligence converts and transforms strings of text, dates, and other variables into meaningful patterns and information that the ML system uses to deliver results.

Algorithm and Framework Support

Modern ML platforms typically support multiple algorithms and frameworks; this flexibility is crucial. In some cases, dozens or hundreds of algorithms may be required for a business process. Yet, it’s also important to have automated algorithm selection capabilities that suggest and match algorithms with tasks. This feature typically reduces complexity and improves ML performance. Additionally, having access to a range of framework options gives users more agility when automating ML development tasks.

Training and Tuning Tools

It’s vital to determine how well algorithms function and what business value the ML framework delivers. Most users benefit from smart hyperparameter tuning, which simplifies the ability to optimize each algorithm. Various packages include different tools and capabilities, and, not surprisingly, some work better for certain types of tasks and algorithms. Especially with large language models and other larger ML models, you’ll want to identify tools that make training and fine-tuning easy, regardless of your particular use cases.

Ensembling Tools

Within ML, it’s common to rely on multiple algorithms to accomplish a single task. This helps balance out strengths and weaknesses and minimize the impacts of data bias. Ensembling refers to the process of integrating and using different algorithms effectively and is an important feature to look for in ML platforms.

Competition Modeling

Since there is no way to know how an algorithm or ML model works before it’s deployed, it’s often necessary to conduct competition modeling. As the name implies, this pits multiple algorithms against each other to find out how accurate and valuable each is in predicting events. This leads to the selection of the best algorithms.

Deployment Tools

Putting an ML model into motion can involve numerous steps—and any error can result in subpar results or even failure. To prevent these kinds of issues, it’s important to ensure that an ML platform offers automation tools and, for some situations, one-click deployment. Many top-tier tools also offer both experimental and production-focused deployment workflows and support.

Dashboards and Monitoring

It’s essential to have visibility into the machine learning model’s performance and how it works, including the algorithms that are running and how they are evolving to meet new needs over time. Dashboards and monitoring tools are particularly effective in this area, especially if they come with filters and visual elements that help all stakeholders review important data. Having this kind of visibility helps an organization add, subtract, and change ML models as needed.

Also see: Top Data Visualization Tools

Benefits of Machine Learning Platforms

Organizations that use machine learning platforms to develop their ML models can create models on a greater scale, at a greater speed, and with higher levels of accuracy and utility. Some of the most common benefits that come from using machine learning platforms include the following:

• End-to-end ML: Many platforms take an end-to-end approach and give you all the tools you need to manage the full ML development and deployment lifecycle.
• ML model organization: The unified platform makes it easier to organize, find, and retrieve new and old ML models.
• Flexibility and extensibility: Users can work with various frameworks, software libraries, and programming languages to produce a model that fits their needs.
• Features for ease of use: Low-code/no-code tools are often available to simplify model development, deployment, and monitoring.
• Automation capabilities: Automation workflows can be set up for various areas of the ML lifecycle, simplifying, standardizing, and speeding up the entire process.
• Scalable platform capabilities: Several platforms work with big-data ML training sets and goals, including for large language models.
• Governance and ethical considerations: A growing number of ML vendors are incorporating model governance, cybersecurity, and other responsible frameworks into their platforms to make ML modeling a more ethical and manageable process.

Also see: Data Mining Techniques

How to Choose the Best Machine Learning Software

While it’s possible to build a custom ML system, most organizations rely on a dedicated machine learning platform from an ML, data science, or data analytics vendor. It’s best to evaluate your organization’s needs, including the type of machine-learning technology you require, before making your selection. Consider whether your organization would benefit from a classical method or deep learning approach, what programming languages are needed, and which hardware, software, and cloud services are necessary to deploy and scale a model effectively.

Another of the most important decisions you can make revolves around the underlying machine learning frameworks and libraries you choose. There are four main options to consider in this area:

• TensorFlow: An open-source and highly modular framework created by Google.
• PyTorch: A more intuitive open-source framework that incorporates Torch and Caffe2 and integrates with Python.
• scikit-learn: A user-friendly and highly flexible open-source framework that delivers sophisticated functionality.
• H2O: An open-source ML framework that’s heavily slanted to decision support and risk analysis.

Other key factors to consider when choosing an ML platform include available data ingestion methods, built-in design tools, version control capabilities, automation features, collaboration and sharing capabilities, templates and tools for building and testing algorithms, and the quantity and variety of compute resources.

Throughout the selection process, keep in mind that most of today’s platforms offer their solutions within a platform-as-a-service (PaaS) framework that includes cloud-based machine learning software and processing along with data storage and other tools and components. Pay close attention to how much support is offered through this model and if any community-driven support or training opportunities are included to help you get started.

Also see: Top AI Software

Review Methodology

The platforms in this machine learning platform review were assessed through a combination of multiple research techniques: combing through user reviews and ratings, reading whitepapers and product sheets, considering the range of common and differentiating features listed on product pages, and researching how each tool compares across a few key metrics. More than 25 platforms were assessed before we narrowed our list to these top players.

eWeek chose the top 10 selections in this list based on how well they addressed key feature requirements in areas like advanced data processing and management, feature engineering, model training and fine-tuning, performance monitoring, and reporting and analytics.

Beyond key features, we also considered how well each tool would meet the needs of a wide range of enterprise user audiences, whether your primary user is an experienced ML developer or data scientist or a non-technical team member who needs low-code model-building solutions. Finally, we looked at the affordability and scalability of each tool.

Bottom Line: Selecting the Best Machine Learning Solution for Your Business

The right ML solution for your business may end up being a combination of multiple solutions, as different platforms bring different strengths to the table. Some of these tools particularly excel at preparing data for high-quality model development. Others provide the frameworks and integrations necessary to build the model. Still others offer recommendations and managed support to help you optimize existing models for future performance goals.

With so many of these tools not only integrating well with each other but also available in free and/or open-source formats, it may well be worth the time it would take to incorporate multiple of these leading tools into your existing machine-learning development strategies.

Read next: Top 9 Generative AI Applications and Tools

The post 10 Best Machine Learning Platforms appeared first on eWEEK.

Especially as machine learning models and their capabilities become more entwined with regular business operations, a growing number of AI/ML and tech teams are shifting their operational processes to an MLOps approach.

Let’s go in-depth on what MLOps is, how it works in practice, and what you can do to optimize your MLOps strategy from the beginning.

What Is MLOps?

MLOps, or machine learning operations, is a diverse set of best practices, processes, operational strategies, and tools that focus on creating a framework for more consistent and scalable machine learning model development lifecycles.

The idea is that, by giving more teams visibility into and control over the development lifecycle — along with adding more structured standards for how these models get deployed and reworked over time — a higher quality, scalable, and repeatable model will be created.

At its core, MLOps intends to be a collaborative effort among the different technical and operations teams that work on machine learning models. As such, MLOps best practices are typically formulated by a combined team of:

• Data scientists
• Machine learning engineers
• IT team members
• DevOps engineers
• Leaders from across business operations and verticals

The goal is to create a process that works well for every team’s project workflows while aligning with greater organizational budgets, goals, and best practices.

MLOps has grown in popularity not only because of its focus on standardization and collaboration but also because of the wide breadth of ML development areas the operational best practices can cover. Generally speaking, MLOps is applied to model building and design, model deployment, data management and integration, project management, model maintenance, and other areas related to ML model lifecycle management.

To cover this range of areas within ML model development, MLOps frequently employs automation and other DevOps best practices to eliminate more tedious task work, standardize project workflows, and more quickly and scalably deliver a functional ML model.

The lifecycle of an MLOps deployment

MLOps vs. DevOps

MLOps and development for operations (DevOps) are both best practice frameworks that focus on making a streamlined, automated, and scalable development cycle, but DevOps is a broader version of the concept.

While MLOps is applied primarily to machine learning models and the teams, tasks, and best practices that go into optimizing ML models, DevOps is a set of best practices that can be applied to any software development project or lifecycle in the IT operations realm.

MLOps vs. AIOps

Artificial intelligence for IT operations, or AIOps, relates to the AI-driven automations that can be applied to various IT operations and DevOps projects.

In order for AIOps to actualize useful automations in areas like data analytics, resource optimization, and more, the practice relies on a combination of DataOps and MLOps to collect and prepare usable data and develop usable ML models. Although MLOps is typically considered a subset of what happens in an AIOps framework, in many cases, AIOps is also applied to MLOps projects to automate ML model analysis and monitoring.

Learn more: 10 AIOps Best Practices

MLOps vs. LLMOps

Large language model operations (LLMOps) is an emerging subarea of MLOps that focuses on machine learning best practices, automations, tools, and operational standards for managing LLM development. As a growing number of organizations engage with generative AI models, whether they build their own or fine-tune third-party models, LLMOps offers focused guidance for developing repeatable and scalable LLM iterations.

Pros and Cons of MLOps

Pros of MLOps

• Standardized, efficient ML model development lifecycles: When standardized cross-team and cross-project processes and tools are in place for ML model development, your team can ensure that consistent models are produced on a regular basis. This can also make the CI/CD (continuous integration / continuous development) cycle and other steps like testing and monitoring more efficient because everyone is on the same page.
• Cross-team collaboration and development: Because standards and developmental best practices have been documented and merged from across teams and disciplines, your machine learning models will be informed by everyone’s best practices and project use cases. You’ll also be able to ensure that no unnecessary duplicate work is going on in silos around your business.
• Higher-quality ML models: Beyond benefiting from cross-team best practices, ML models tend to improve in quality when MLOps is applied because MLOps focuses on creating reproducible results at all stages of model development. With the right MLOps tools and plans in place, organizations improve model governance, performance monitoring, and development environment quality, for example.
• Scalable processes and documentation: The standardized processes and scalable infrastructure that come with MLOps make it possible for organizations to scale their ML model development operations, working with larger datasets and more complex model types. Version control and documentation are both key aspects of MLOps that ensure users keep track of their progress and learn from historic iterations of ML model development to make bigger and better models moving forward.
• Automation: A big part of MLOps involves automating tedious, repeated task work that can bog down your team. Automation can prevent your team from unintentionally adding new errors to the mix and deviating from standardized, compliant procedures. Perhaps even more significantly, automation can free up time for your team to focus on more strategic model development and management tasks.

Cons of MLOps

• Requires in-house or third-party expertise: If you don’t already have a team of developers, data scientists, and ML specialists on staff, it can be difficult to develop and adhere to MLOps procedures and tools that make sense for your production goals. If you’re planning on bringing these types of employees onto your staff soon, it may be worth waiting for their input on an MLOps process before committing.
• The cost of MLOps infrastructure, tools, and resources: When you decide to adopt an MLOps strategy, you may end up needing to invest in new tools and resources for data integration, data pipelines, real-time monitoring and analytics, and more. While free and low-cost versions of many MLOps tools are available, moving to MLOps can still be an incredibly expensive endeavor, especially when you consider the scale and cost of compute resources that may be required.
• The multiplication of user errors with automation: Automation is a double-edged sword in that it can either minimize or multiply user errors, depending on how it’s used. If you don’t do a thorough data quality check at the beginning of your MLOps lifecycle and on an ongoing basis thereafter, it’s entirely possible you could increase the rate and severity of a small error in your dataset.
• Somewhat limited agility: It’s true that a key benefit of adopting a DevOps-based strategy is getting to work within an agile methodology, but in practice, there are some limits to MLOps agility. It is certainly a scalable framework, but it’s less agile in the sense that the framework’s rules for collaboration and development may make it more difficult to pivot away from tried-and-true projects to newer project concepts.
• The difficulty of ongoing data management: As ML models and their training data and inputs grow in scale, it can get difficult to manage the quality and reliability of all that data. This kind of data sprawl can make it challenging to produce high-quality results, not to mention making it much more difficult to ensure all data is being used ethically and in compliance with relevant data privacy regulations.

Leading MLOps Tools and Solutions

A number of end-to-end machine learning platforms, data integration and management solutions, and open-source and closed-source tools currently support MLOps best practices and workflows. Depending on your current tooling portfolio and expertise, it’s possible your team could benefit from using more than one of the following best MLOps tools and solutions:

• MLflow: An open-source machine learning platform that includes a central model registry and APIs, integrations, and tools for ML lifecycle management. With Apache Spark, the platform can scale to work with big data workloads.
• Amazon SageMaker: A fully managed ML platform from AWS with a variety of tools and frameworks that support ML model building, training, and deployment. The solution is designed to work with a range of data formats, applications, and programming languages.
• TensorFlow: An open-source machine learning platform and library that supports data preparation, model building and deployment, data automation, performance monitoring, and other key facets of MLOps. Users can build their own models or work off of prebuilt TensorFlow models.
• Iguazio: An MLOps-specific platform that primarily focuses on automated ML pipelines but also offers solutions for model monitoring, CI/CD, data mesh, and generative AI. A handful of open-source solutions, including MLRun and Nuclio, are available as well.
• Weights & Biases: An AI developer and MLOps platform that supports big-data-driven MLOps scenarios and projects, including for LLMs, computer vision, and recommendation systems. The platform includes extensive features for experiment tracking and model versioning as well.
• Neptune.ai: An MLOps experimentation platform that gives users access to model versioning, pipeline building, logging, and artifact tracking features. Paid plans also include advanced analytics and access controls.
• H2O MLOps: An end-to-end MLOps solution that includes automated scaling and drift detection capabilities. Users can monitor and deploy models across various languages, frameworks, and formats.
• Flyte: A workflow orchestration platform that can be used for machine learning, analytics, AI orchestration, and bioinformatics. This is considered a particularly user-friendly solution for data scientists and data engineers.

A typical MLOps deployment hosted in the cloud. 

Learn about similar tools and solutions in these guides:

• Best MLOps Tools & Platforms
• Best Machine Learning Platforms

MLOps Best Practices

When getting started with MLOps, it’s important to consider the best practices that DevOps practitioners follow and gear them to more specific ML model development use cases. The following best practices include a mixture of DevOps and MLOps-specific tips and tricks for better results and organization-wide adoption:

Complete a comprehensive design phase

The design phase is the earliest stage of MLOps-driven model development, when data scientists, machine learning engineers, and other relevant stakeholders come together to design their ideal model architecture and determine what’s needed to set that plan in motion.

The design phase should not only consider what problems you’re trying to solve and what type of model and model training is most appropriate; it should also include data collection and preparation, feature and requirements engineering, and detailed documentation for every decision you make.

At this stage, you should also take an inventory of the current tools and resources you have and any that are missing, identifying any additional costs or complications that may come with these new investments.

Automate when and where it makes sense

Automation is a large part of what makes MLOps repeatable and scalable, but it’s important to be thoughtful about what, when, and how you automate. First and foremost, make sure the data and rules you set up for an automation are all accurate and error-free so you don’t unintentionally multiply any existing errors. After completing your data quality check, begin with smaller-scale automations and test them out — ideally in a test environment — to determine if any optimizations need to be made.

Everything from dataset validation to model drift to performance monitoring can be automated with the right tools and setup, so consider what your teams’ greatest pain points are today and how automations can help them be more efficient.

Continuously monitor and test MLOps performance

MLOps is built on the idea that, through a cycle of continuous integration and development, better models can be created and standardized over time. But to continually improve, you need to continuously monitor and test current model performance and identify areas for improvement and additional training.

If you’re not sure what to monitor for when testing MLOps performance, consider creating metrics based on these important performance elements:

• Data and concept drift
• Model confidence
• Model accuracy and precision
• Model bias
• Model recall and history logs
• Performance lags and latency
• Explainability
• User feedback (if relevant)

Invest in proven, highly-rated MLOps tools

MLOps tools can help you automate different MLOps tasks, including data quality management, ML model monitoring, real-time analytics, and more.

Investing in one or a few of the tools that we covered above is a great way to ensure all members of your team, regardless of their development experience, can achieve greater visibility into the MLOps lifecycle and their role in its success.

Get buy-in from and provide training for relevant team members

MLOps is only as successful as the teams that work on these models make it. That’s why it’s important to roll out MLOps thoughtfully, bringing together data, development, operations, and other teams in a way that facilitates conversation and collaboration.

It will be a difficult transition for many of these teams, especially if they haven’t traditionally worked together, so be sure to provide a range of learning resources, hands-on practice projects, and training opportunities for more effective organizational change management.

Quality-check data, automations, and all processes

MLOps standards can spiral out of control if you don’t have dedicated team members checking the quality of training data, automations, processes, and other facets of model development. A QA team or specialist who is dedicated to this kind of task work can identify errors and vulnerabilities before they cause bigger problems. A number of MLOps platforms also include quality management and automation tools that can help.

Document your work and project structures

MLOps is not about producing one great ML model but about creating the strategic framework and foundations for multiple great ML models. Even if your MLOps journey starts out rocky, it’s necessary to document every step you take along the way, in regards to data preparation, model development, model deployment, and everything in between. This kind of documentation supports reproducibility and scalability while giving your team the historical data it needs to improve on past decisions.

Keep in mind that documentation is a continual process in MLOps. Each time your team makes a change to a model or process, that change should be documented, ideally in a real-time, cloud-based system where all stakeholders can see evidence of that change and how it impacts everything else.

Bottom Line: Bringing MLOps Into Your Business Workflows

MLOps can be incorporated into any business’s ML model development practice, whether you’re just getting started with machine learning or are looking to transform large-scale operations currently in motion.

As more enterprises work to streamline and monetize machine learning model development for both internal and customer-facing operations, MLOps has grown into a full-fledged best practices framework that helps these business’ technical players stay on track and collaborate more effectively throughout the development lifecycle.

Fleshing out and committing to a standardized MLOps program can take some time and be difficult, especially when you consider the organizational change management that goes into this kind of transition. However, understanding the MLOps challenges you may face and going in with a definitive game plan and best practices like the ones listed above is a good first step toward successful implementation that scales.

Read next: 13 Best Artificial Intelligence (AI) Software

The post What is MLOps? An Overview of Machine Learning Operations appeared first on eWEEK.

Open source intelligence is sourced from all corners of the web, and while that makes the data incredibly comprehensive, it also brings forth a large body of data that needs to be fact-checked and reviewed closely for the best possible results.

Let’s take a closer look at what open-source intelligence is, how it works, and how you can apply this type of intelligence to your business operations most effectively.

What Is Open Source Intelligence?

Open source intelligence is a type of data-driven intelligence that scours the internet and other public sources for information that’s relevant to a user’s query or search. Most often, OSINT is used to strategically collect information about a particular individual, group of people, organization, or other public entity.

Historically, OSINT developed before the internet and was a military espionage technique for finding relevant information about military enemies in newspapers, radio broadcasts, and other public data sources. While most data sources used for OSINT today are online or somehow digitized, OSINT analysts still have the option to collect physical data from public, open sources.

Also see: Top Data Visualization Tools

Passive vs. Active OSINT

Passive and active OSINT are both viable open source intelligence collection methods with different amounts of hands-on activity and in-depth research required.

With passive OSINT, users most often complete a simple search engine, social media, or file search or look at a website’s or news site’s homepage through a broad lens. They aren’t actively trying to collect highly specific information but rather are unobtrusively looking at the easiest-to-find, top-of-the-stack intelligence available. With this intelligence collection method, the goal is often to collect useful information without alerting targets or data sources to your intelligence collection activities.

When practicing active OSINT, the methods tend to be more intrusive and involved. Users may complete more complex queries to collect obscure intelligence and metadata from databases and network infrastructure, for example. They also might fill out a form or pay to get through a paywall for more information.

In some cases, active OSINT may even involve reaching out directly to sources for more information that is not publicly available or visible. While active OSINT is more likely to give users real-time, in-depth information than passive OSINT, it is much more difficult to do covertly and may lead you to legal troubles if your data collection methods aren’t careful.

Open Source Intelligence Data Sources

Open source intelligence can be sourced from any public dataset or property. These are some of the most common OSINT data sources from across the web:

• Social media platforms
• Public-facing websites
• News media
• Academic and scientific studies
• Internet of Things databases
• Business directories
• Financial reports
• Images and image libraries
• Public records, both digital and physical

Also see: Best Data Analytics Tools 

How Does Open Source Intelligence Work?

For individuals and organizations that want to take advantage of open source intelligence, a simple way to get started is with a search engine query. Often, asking the right question about the demographic information you need is the first step to finding relevant open source data entries that can lead to more detailed information.

Beyond using search engines for internet-wide data searches, you can also refine and focus your search on specific data platforms or databases, such as a certain social media platform. Depending on your goals and experience, you may also benefit from analyzing open source threat intelligence feeds and other sources that frequently update massive amounts of data.

If your data collection and analysis goals require you to work with big data sources like databases, data lakes, or live feeds, manual searches and research are ineffective. To quickly process and sort through large amounts of intelligence, you’ll want to consider investing in a web scraping or specialized OSINT tool that can automate and speed up the data analysis process.

OSINT Use Cases

Have you ever “Facebook stalked” someone you just met or Google searched your family’s last name to see what pops up? Both of these are simple examples of how even individuals practice a simplified form of open source intelligence in their daily lives.

Businesses, too, may collect OSINT without realizing it, but in most cases, they are collecting this kind of intelligence for a distinct competitive advantage or cause. Here are some of the most common OSINT use cases in practice today:

• Threat intelligence, vulnerability management, and penetration testing: Especially when used in combination with more comprehensive threat intelligence platforms, open source intelligence and data collection can give security analysts and professionals a more comprehensive picture of their threat landscape, any notable threat actors, and historical context for past vulnerabilities and attacks.
• Market research and brand monitoring: If you want to get a better look at both quantitative purchase histories and overall brand sentiment from customers, OSINT is an effective way to collect broad demographic intelligence about how your brand is performing in the eyes of the consumer. For this particular use case, you may conduct either passive or active OSINT in social media platforms, user forums, CRMs, chat logs, or other datasets with customer information.
• Competitive analysis: In a different version of the example above, you can complete OSINT searches on competitor(s) to learn more about how they’re performing in the eyes of customers.
• Geolocation data sourcing and analysis: Publicly available location data, especially related to video and image files, can be used to find an individual and/or to verify the accuracy of an image or video.
• Real-time demographic analyses over large populations: When large groups of people are participating in or enduring a major event, like an election cycle or a natural disaster, OSINT can be used to review dozens of social media posts, forum posts, and other consumer-driven data sources to get a more comprehensive idea of how people feel and where support efforts — like counterterrorism or disaster relief response, for example — may be needed.
• Background checks and law enforcement: While most law enforcement officials rely on closed-source, higher intelligence feeds for background checks and identification checks, OSINT sources can help fill in the blanks, especially for civilians who want or need to learn more about a person. Keep in mind that there are legal limits to how open source intelligence can be used to discriminate in hiring practices.
• Fact-checking: Journalists, researchers, and everyday consumers frequently use OSINT to quickly check multiple sources for verifiable information about contentious or new events. For journalistic integrity and ethical practice, it’s important to collect information directly from your sources whenever possible, though OSINT sources can be a great supplement in many cases.

Also read: Generative AI: 15 Enterprise Use Cases You Can Implement

10 OSINT Tools and Examples

Particularly for passive OSINT and simple queries, a web scraping tool or specialized “dork” query may be all that you need. But if you’re looking to collect intelligence on a grander scale or from more complex sources, consider getting started with one or several of the following OSINT tools:

1. Spyse: An internet asset registry that is particularly useful for cybersecurity professionals who need to find data about various threat vectors and vulnerabilities. It is most commonly used to support pentesting.
2. TinEye: A reverse image search engine that uses advanced image identification technology to deliver intelligence results.
3. SpiderFoot: An automated querying tool and OSINT framework that can quickly collect intelligence from dozens of public sources simultaneously.
4. Maltego: A Java-based cyber investigation platform that includes graphical link analysis, data mining, data merging, and data mapping capabilities.
5. BuiltWith: A tool for examining websites and public e-commerce listings.
6. theHarvester: A command-line Kali Linux tool for collecting demographic information, subdomain names, virtual host information, and more.
7. FOCA: Open source software for examining websites for corrupted documents and metadata.
8. Recon-ng: A command-line reconnaissance tool that’s written in Python.
9. OSINT Framework: Less of a tool and more of a collection of different free OSINT tools and resources. It’s focused on cybersecurity, but other types of information are also available.
10. Various data analysis and AI tools: A range of open source and closed source data analysis and AI tools can be used to scale, automate, and speed up the process of collecting and deriving meaningful insights from OSINT. Generative AI tools in particular have proven their efficacy for sentiment analysis and more complex intelligence collection methods.

More on a similar topic: Top 9 Generative AI Applications and Tools

Pros and Cons of Open Source Intelligence

Pros of OSINT

• Optimized cyber defenses: Improved risk mitigation and greater visibility into common attack vectors; hackers sometimes use OSINT for their own intelligence, so using OSINT for cyber defense is often an effective response.
• Affordable and accessible tools: OSINT data collection methods and tools are highly accessible and often free.
• Democratized data collection: You don’t need to be a tech expert to find and benefit from this type of publicly available, open source data; it is a democratized collection of valuable data sources.
• Quick and scalable data collection methods: A range of passive and active data sourcing methods can be used to obtain relevant results quickly and at scale.
• Compatibility with threat intelligence tools and cybersecurity programs: OSINT alone isn’t likely to give cybersecurity professionals all of the data they need to respond to security threats, but it is valuable data that can be fed into and easily combined with existing data sources and cybersecurity platforms.

Cons of OSINT

• Accessible to bad actors and hackers: Just like your organization can easily find and use OSINT, bad actors can use this data to find vulnerabilities and possible attack vectors. They can also use OSINT-based knowledge to disrupt and alter intelligence for enterprise OSINT activity.
• Limitations and inaccuracies: Public information sources rarely have extensive fact-checking or approval processes embedded into the intelligence collection process. Especially if multiple data sources share conflicting, inaccurate, or outdated information, researchers may accidentally apply misinformation to the work they’re doing.
• User error and phishing: Users may unknowingly expose their data to public sources, especially if they fall victim to a phishing attack. This means anyone from your customers to your employees could unintentionally expose sensitive information to unauthorized users, essentially turning that private information into public information.
• Massive amounts of data to process and review: Massive databases, websites, and social media platforms may have millions of data points that you need to review, and in many cases, those numbers are constantly growing and changing. It can be difficult to keep up with this quantity of data and sift through it to find the most important bits of intelligence.
• Ethical and privacy concerns: OSINT is frequently connected without the target’s knowledge, which is an issue with AI and ethics. Depending on the data source and sourcing method, this information can be used to harm or manipulate people, especially when it’s PII or PHI that has accidentally been exposed to public view.

Bottom Line: Using OSINT for Enterprise Threat Intelligence

Getting started with open source intelligence can be as simple as conducting a Google search about the parties in question. It can also be as complex as sorting through a publicly available big data store with hundreds of thousands of data entries on different topics.

Regardless of whether you decide to take a passive or active approach, make sure all members of your team are aware of the goals you have in mind with open source intelligence work and, more importantly, how they can collect that intelligence in a standardized and ethical manner.

Read next: 50 Generative AI Startups to Watch in 2023

The post Open Source Intelligence (OSINT) Guide appeared first on eWEEK.

AI certifications demonstrate understanding and competence in various aspects of AI, such as machine learning, natural language processing, computer vision, robotics, and AI software.

The demand for professionals with AI certifications is great: The artificial intelligence market size is projected to grow from $515.31 billion in 2023 to more than $2 trillion by 2030, at a scorching CAGR of 21.6% – and with this growth comes enormous demand for AI experts.

This guide analyzed the best AI certifications to help you advance your careers and stay up to date with the latest AI technologies.

Best AI Certification: Comparison Chart

Here is a head-to-head summary chart of the best AI certification courses with the features, certifying institutions and prices.

Best 20 AI Certifications 2023

AI certification programs usually involve completing training courses, passing assessments or exams, and meeting specific criteria set by certifying bodies or organizations.

AI for Everyone (Coursera)

Non-technical professionals looking for a beginner-friendly AI certification course may find the AI for Everyone course beneficial. The course is hosted on Coursera and taught by Andrew Ng, courtesy of DeepLearning.AI.

This course is designed to provide a comprehensive introduction to artificial intelligence (AI) concepts, terminology, and applications without requiring any prior technical knowledge. It aims to equip non-technical professionals with the necessary understanding and skills to navigate the AI landscape.

The course consists of approximately 10 hours of video lectures, quizzes, and exercises to reinforce learning. Upon completion, students will receive a certificate from Coursera, which can be shared on professional platforms.

Experience required

This course has no prerequisites, making it suitable for both technical and non-technical individuals.

Course content

This course has four modules:

• What is AI? – 2 hours to complete.
• Building AI Project –  2 hours to complete.
• Building AI In Your Company –  3 hours to complete.
• AI and Society –  2 hours to complete.

Key course details

Also see: 100+ Top AI Companies 2023

Certified Artificial Intelligence Scientist (CAIS)

The Certified Artificial Intelligence Scientist course offered by the United States Artificial Intelligence Institute is one of the top certifications in artificial intelligence. This comprehensive program covers various AI topics, such as machine learning, deep learning, and Computer Vision with Reinforcement Learning. CAIS also provides hands-on training and real-world projects to enhance practical skills in AI.

Experience required

• PATH 01: Bachelor’s degree or equivalent in any academic discipline and at least five years of experience in AI, ML, Data Science, Business Analytics, Business Intelligence, Project Management, or any Programming language.
• PATH 02: ​​Master’s degree or equivalent in any academic discipline and at least four years experience in AI, ML, Data Science, Business Analytics, Business Intelligence, Engineering, Finance, and Management.
• PATH 03: Must have completed CAIC or other equivalent certification and at least four years experience for Bachelor’s Degree holders and three years experience for Masters Degree holders.

Course content

This exam includes the following general domains and their percentage weights on the exam:

• Artificial Intelligence and Machine Learning (18%).
• Strategic data science and management (15%).
• The economics of AI (12%).
• Supervised deep learning and computer vision (18%).
• AI in marketing (10%).
• Reinforcement learning (RL) (12%).
• Artificial Intelligence, cloud, and security (15%).

Key course details

Also see: Top Generative AI Apps and Tools

Computer Science for Artificial Intelligence

Beginners new to the field of computer science and AI may want to consider the Computer Science for Artificial Intelligence course offered by three experts from Harvard University: Doug Lloyd and Brian Yu, who are Senior Preceptors in Computer Science at Harvard University, and David J. Malan, a Gordon McKay Professor of the Practice of Computer Science at Harvard University.

This self-paced Harvard certificate includes two courses, including CS50’s Introduction to Computer Science and CS50’s Introduction to Artificial Intelligence with Python. The course provider claims learners can finish the certification program in five months.

The course offers hands-on projects, which can help learners will gain exposure to the theory behind graph search algorithms, classification, optimization, reinforcement learning, and other topics in artificial intelligence.

Experience required

This course doesn’t have prerequisites, meaning individuals of all levels can apply. However, a basic understanding of computer programming concepts would be beneficial.

Course content

• Understanding of computer science and programming.
• Graph search algorithms.
• Reinforcement learning.
• Machine learning.
• Artificial intelligence principles.
• How to design intelligent systems.
• How to use AI in Python programs.

Key course details

Fundamentals of Google AI for Web Based Machine Learning

This certification course is designed to help learners understand what AI really is and what it isn’t. Jason Mayes, a Senior Developer Relations Engineer for TensorFlow.js, and Laurence Moroney, Lead AI Advocate – both from Google – cover the relationship between data, machine learning, and artificial intelligence to help you discuss and use these technologies with others.

This program has two courses: Google AI for everyone, which can be completed in 4 weeks, and Google AI for JavaScript developers with TensorFlow.js, which can be completed in 7 weeks.

Experience required

No prior experience is required to take this course.

Course content

• How machine learning works and how ML, AI and deep learning fit together.
• Applied AI and what programming AI looks like.
• Introduction and overview of the TensorFlow.js library and the advantages of using ML in JavaScript.
• Ways to consume existing machine learning models.
• How to write custom models from a blank canvas (Linear Regression, Convolutional Neural Network).
• How to use industry-standard pre-made models for object detection or natural language processing.
• How to convert Python models to TensorFlow.js format to run them client side in a web browser.

Key course details

For more information, also see: Best Machine Learning Platforms

IBM AI Engineering Professional Certificate

Taught by seven experts, this intermediate-level certificate course offered by IBM takes approximately two months at 10 hours a week to complete. It consists of six courses, which will teach learners how to write Python code that implements various classification techniques, including K-Nearest neighbors (KNN), decision trees, and regression trees; image processing and analysis techniques for computer vision problems; and how to build Deep Neural Networks using PyTorch — the last course includes an AI capstone project with deep learning.

By completing this certificate, students will gain the knowledge and skills needed to start a career in AI engineering or further their existing careers.

Experience required

While there is no specific prerequisite for the course, the IBM AI Engineering Professional Certificate is an intermediate-level course which is recommended for individuals with some background knowledge or experience in programming and computer science.

Course content

• Machine Learning with Python.
• Introduction to Deep Learning & Neural Networks with Keras.
• Introduction to Computer Vision and Image Processing.
• Deep Neural Networks with PyTorch.
• Building Deep Learning Models with TensorFlow.
• AI Capstone Project with Deep Learning.

Key course details

Artificial Intelligence A-Z 2023: Build an AI with ChatGPT4 (Udemy)

With over 231,410 students, Artificial Intelligence A-Z 2023 is a best seller on Udemy. It is best for Artificial Intelligence, Machine Learning, or Deep Learning enthusiasts.

The course teaches learners how to make a virtual self-driving car, make an AI to beat games, Q-learning, deep Q-learning, deep convolutional Q-learning and solve real-world problems with AI. The instructors provide learners with downloadable Python code templates for every AI they build in the course.

Experience required

• High School Maths.
• Basic Python knowledge.

Course content

• Part 0: Fundamentals of Reinforcement Learning.
• Part 1: Deep Q-Learning.
• Part 2: Deep Convolutional Q-Learning.
• Part 3: A3C.

Key course details

Artificial Intelligence Engineer (AIE) Certification Process by the Artificial Intelligence Board of America (ARTiBA)

The Artificial Intelligence Engineer (AIE) certification process is offered by the Artificial Intelligence Board of America (ARTiBA), which is a professional membership body dedicated to promoting and advancing artificial intelligence (AI) practices.

To receive the AIE certification, individuals must undergo a structured evaluation process assessing their knowledge and skills in various AI-related domains. The certification process consists of the following steps:

• Check your eligibility.
• Create your Myartiba account.
• AIE registration confirmation.
• Release of AIE learning material.
• Prepare for the exam & register.
• Certification award.

Experience required

ARTiBA has three registration tracks open for the AiE certification.

• AIE Track 1: Learner should have completed an Associate degree or diploma in Computer Science or Information Technology or any other related discipline plus a ​​minimum of 2 years of work history in any of the computing sub-functions.
• AIE Track 2: Learner should have completed a bachelor’s degree in Computer Science or Information Technology or any other related discipline plus a ​​minimum of 2 years of work-history in any computing sub-functions and must have a good understanding of computer programming.
• AIE Track 3: Current and past students have a Master’s degree in Computer Science or Information Technology or any other related discipline plus a ​​minimum two years of work history in any computing sub-functions; must have a good understanding of computer programming.

Course content

• Essentials of Artificial Intelligence & Machine Learning (27%).
• Essentials of AI & ML Programming (21%).
• Essentials of Natural Language Processing (26%).
• Essentials of Neural Networks & Deep Learning (26%).

Key course details

Also see: Best Artificial Intelligence Software 2023

Artificial Intelligence Graduate Certificate

Stanford School of Engineering is the issuing body of the Artificial Intelligence Graduate Certificate. This particular certification process is a bit different from the ones we have analyzed so far: it requires a significant time commitment, as it’s designed like a proper degree course – you should expect an average of 15-20 hours per week for the lecture and homework assignments. To earn the certificate, you must complete one required course and three elective courses and receive a B (3.0) or better in each course.

This program covers various aspects of AI, including the principles and techniques of AI, ML, computational logic, NLP, NLU, robotics, Deep Learning, computer vision and more.

Experience required

• Strong backgrounds in programming (C/C++, python), linear algebra, calculus, as well as statistics and probability.
• Bachelor’s degree with a minimum 3.0 grade point average.
• Each course may have individual prerequisites.

Course content

• Artificial intelligence principles and techniques.
• Machine learning.
• Natural Language Processing with deep learning.
• Computer vision.
• Reinforcement learning.

Key course details

Microsoft Certified: Azure AI Engineer Associate

Azure AI Engineer Associate is a certification offered by Microsoft that validates an individual’s skills in designing and implementing AI solutions using Azure technologies such as Azure Cognitive Services and Azure Applied AI services.

According to Microsoft, this course will allow you to plan and manage an Azure AI solution, implement image and video processing solutions, implement natural language processing solutions, implement knowledge mining solutions, and implement conversational AI solutions.

You can prepare for this certification in two ways: self-paced and instructor-led training.

Experience required

• Knowledge of Python and C# programming.
• Prerequisites vary based on each learning path.

Course Content

• Prepare for AI engineering.
• Provision and manage Azure AI services.
• Process and translate text with Azure AI services.
• Process and translate speech with Azure AI speech services.
• Create a language understanding solution with Azure AI language.
• Build a question-answering solution.
• Build custom text analytics solutions.
• Create conversational AI solutions.
• Create computer vision solutions with Azure AI vision.
• Extract text from images and documents.
• Implement knowledge mining with Azure cognitive search.
• Develop generative AI solutions with Azure Openai service.

Key course details

Also see: Generative AI Startups 

Artificial Intelligence: Business Strategies and Applications

UC Berkeley designed this course for business professionals who are interested in understanding how artificial intelligence (AI) can be leveraged to drive business growth and innovation. The course covers various aspects of AI and its application in different business contexts. It starts by explaining the fundamental concepts of AI and machine learning and then explores neural networks and deep learning, computer vision, and NLP.

Experience required

This course has no specific prerequisites, but knowledge of business management is a plus.

Course Content

• Introduction–AI and business.
• Machine learning basics.
• Neural networks and deep learning.
• Key applications: computer vision & natural language processing.
• Robotics.
• Ai strategy.
• AI and organizations: building your AI team.
• The future of AI in business.

Key course details

AI for Business Leaders

AI for Business Leaders is an online nanodegree program from Udacity that teaches business leaders how to more effectively embed AI and ML technologies into their existing business technologies and strategies. It is considered an intermediate-level training program with real-world, hands-on projects, but beginners in AI who bring extensive business skills and expertise to the table can benefit from this course as well.

Experience required

Certificate students should have familiarity with basic probability and descriptive statistics. Multiple years of experience in a business strategy role is preferred.

Course content

Lessons in this program include the following:

• AI for Business Leaders Executive Program Introduction
• Introduction to AI for Business Leaders
• Getting Help
• The Paradigm Shift
• The Math Behind the magic
• Architectures of AI Systems
• Working with Data
• Accuracy, Bias, and Ethics
• Gathering Feedback
• Thinking Bigger
• Delivering an ML/AI Strategy

A certificate is awarded based on the completion of these lessons and courses.

Key course details

Jetson AI Certification

The Jetson AI Specialist certification from NVIDIA Deep Learning Institute is a certification and training program that gives users a chance to learn and tailor AI skills to Jetson embedded computing technology and similar systems for machine learning applications. Students of this program will learn through a combination of hands-on, open-source projects and project-based assessments.

Experience required

For the Jetson AI Specialist certification, it’s recommended that students have a basic familiarity with Python and Linux. Students of the Jetson AI Ambassador certification program should also have teaching or training experience.

Course content

The Jetson AI Specialist certification requires all students to complete the Jetson AI Fundamentals course, which includes the following sections:

• NVIDIA Deep Learning Institute’s Getting Started with AI on Jetson Nano course
• JetBot (optional)
• Hello AI World

Users who are interested in receiving a certificate must also complete the Getting Started with AI on Jetson Nano DLI course certificate and a project-based assessment.

Key course details

Professional Machine Learning Engineer (Google Cloud)

The Professional Machine Learning Engineer certification from Google Cloud is a skill-based exam and certification that determines how effectively ML engineers can design and architect, develop, automate and orchestrate, and monitor machine learning developments on Google Cloud platforms and technologies. No coursework is required to earn this certification, however many users elect to complete a relevant Google Cloud training path to prepare for the exam.

Experience required

No prior experience is required, though it’s a good idea to have at least three years of relevant experience and at least one year of experience working with relevant Google Cloud technologies.

Course content

No specific coursework is required to complete the 50 to 60 multiple choice and multiple select questions that make up the certification exam, which can be taken in person or online. However, many students start with the exam guide and sample questions or complete the Machine Learning Engineer learning path, which includes online and in-person training courses.

Key course details

Post Graduate Program in AI and Machine Learning

The Post Graduate Program in AI and Machine Learning is a collaboration among Simplilearn, Purdue University, and IBM for business professionals who want to improve their AI and ML knowledge and skills through an online bootcamp format. Many users select this certification program for its collaborative learning format and for the blend of academic and applied business AI knowledge they can gain from Purdue and IBM.

Experience required

Students of this bootcamp program should have at least two years of work experience, a bachelor’s degree, and a basic understanding of mathematics and programming.

Course content

The program is split into foundations, core, capstone, and elective courses:

• Foundations: Mathematics & Statistics Essentials
• Foundations: Programming Refresher
• Foundations: Python for Data Science (IBM)
• Core: Applied Data Science with Python
• Core: Machine Learning
• Core: Deep Learning with TensorFlow (IBM)
• Core: Deep Learning Specialization
• Core: Essentials of Generative AI, Prompt Engineering & ChatGPT
• Capstone
• Elective master classes and advanced courses on computer vision, NLP, speech recognition, reinforcement learning, etc.

Key course details

AI Applications for Growth

AI Applications for Growth from Northwestern University’s Kellogg School of Management is an executive education program that teaches business leaders how to use AI and ML in a way that benefits the business and customers alike. It is an online program that is primarily designed for C-level executives and business leaders across sales, marketing, and IT roles.

Experience required

No prior experience is required, though the program is catered to the experience and workplace scenarios of business executives.

Course content

The program is primarily divided into eight module topics:

• Module One: the AI Revolution: Trends, Tools, and Applications
• Module Two: AI and Customer Experience Management
• Module Three: AI and Operations Management
• Module Four: AI and Business Support Functions
• Module Five: AI Applications in Select Industries
• Module Six: AI Applications in Autonomous Vehicles and Transportation
• Module Seven: Transforming Your Business with AI: Strategy and Capabilities
• Module Eight: Transforming Your Business with AI: Organization and Society

Key course details

Professional Certificate Program in Machine Learning & Artificial Intelligence

The Professional Certificate Program in Machine Learning & Artificial Intelligence is a professional education certification program from MIT that emphasizes the applications of AI and ML in a few emerging areas, like the Internet of Things (IoT), smart manufacturing, computer vision, and industry-specific automations and analytics. It is ideally designed for midlevel business leaders who still manage some of the day-to-day operations that AI touches.

Experience required

Though no specific experience is required, the program is designed for business professionals and tech leaders with at least three years of professional experience and at least a bachelor’s degree in a technical field.

Course content

All students must complete the five days of core coursework in the following two courses:

• Machine Learning for Big Data and Text Processing: Foundations
• Machine Learning for Big Data and Text Processing: Advanced

Students must complete an additional 11 days of coursework from the program’s elective catalog:

• Advanced Data Analytics for IIoT and Smart Manufacturing
• AI for Computational Design and Manufacturing
• AI Strategies and Roadmap: Systems Engineering Approach to AI Development and Deployment
• Applied Data Science Program: Leveraging AI for Effective Decision-Making
• Bioprocess Data Analytics and Machine Learning
• Deep Learning for AI and Computer Vision
• Designing Efficient Deep Learning Systems
• Foundations of Data and Models: Regression Analysis
• Graph Algorithms and Machine Learning
• Machine Learning for Healthcare
• Machine Learning for Materials Informatics
• Reinforcement Learning
• Advanced Reinforcement Learning

Key course details

AI for Non-Technical People: A Hands-On Beginner’s Course

AI for Non-Technical People: A Hands-On Beginner’s Course is a short AI training course from Udemy that is designed specifically for business professionals and individuals with no AI or technical experience. The program focuses on the history of AI, practical applications of AI, and the basics of how to build an AI model. Compared to most courses in this guide, this is a great starting point if you’re simply interested in AI but don’t have any foundational skills or experience in related subject matter.

Experience required

No experience is necessary to complete this course and certification.

Course content

The course is divided into four main modules:

• Introduction to Artificial Intelligence
• Fundamentals of AI
• Ethical Considerations
• Hands On Project

In the hands-on project portion, users will receive the training to build and test an audio model.

Key course details

Intel Edge AI Certification

The Intel Edge AI Certification is a certification program offered by Intel to users who want to demonstrate their hands-on skills with Intel edge, AI, and other developer tools. The certification program is less focused on traditional coursework and more focused on hands-on projects and skill development. Though this certification is highly specific to Intel and its products, it’s a good training ground for anyone who wants a more technical training program.

Experience required

Several prerequisite are required to complete this Intel certification:

• Intermediate Python programming knowledge
• Experience working with JupyterLab or Jupyter Notebook
• Knowledge of CNN-based architectures and deep learning layers
• Experience working with the Linux command line
• Experience with OpenCV

Course content

Rather than taking premade coursework, students of this certification will complete specific projects and build their own edge AI solutions portfolio. These are some of the projects that are part of this certification program:

• Supermarket Retail Loss Prevention with Classification
• Industrial Worker Safety with Object Detection
• Smart City Data Augmentation with Style Transfer

Key course details

Graduate Certificate in Ethical Artificial Intelligence

The Graduate Certificate in Ethical Artificial Intelligence from San Francisco State University is a more academic approach to AI certification that considers the ethics of AI from all angles, using courses in computer science, business management, and philosophy to illustrate the importance of ethical practices with emerging technologies. The course culminates in a 10-page research paper, making this a much more collegiate and academic program than most others on this list.

Experience required

Students of this program must be current graduate students at SFSU or be willing to apply for admission into a graduate business certificate program and have at least a bachelor’s degree. Ideally, students will come from a philosophy, business, or computer science background, as these are the primary disciplines covered in the program.

Course content

One course from each of the following categorical groups must be completed to earn this graduate certificate:

• AI Technologies and Applications (computer science coursework)
  • AI Explainability and Ethics
  • Data Mining
  • Pattern Analysis and Machine Intelligence
• Business Ethics and Regulatory Compliance (management coursework)
  • Ethics and Compliance in Business
• Ethical Principles (philosophy coursework)
  • Philosophy and Current Applications of Artificial Intelligence
  • Philosophical Issues in Artificial Intelligence

All students must also complete an independent study and research/reflection paper from the perspective of their chosen discipline.

Key course details

ChatGPT / AI Ethics: Ethical Intelligence in an AI World

ChatGPT / AI Ethics: Ethical Intelligence in an AI World is a short certification course and program from Udemy that focuses on ethical AI from the angle of some of the emerging generative AI tools that are becoming popular in business and everyday use. Upon completion of this course, students should be able to apply ethical principles and best practices to the usage of ChatGPT and similar generative AI platforms.

Experience required

No experience is necessary to complete this course and certification.

Course content

The course is divided into four main sections with multiple lectures each:

• Introduction
• The 5 Ethical Principles Applied to Working with AI / Artificial Intelligence
• Ethics in the Age of AI: Unraveling Deeper Issues Amidst Current Concerns
• Concluding Section

Key course details

Benefits of AI Courses and Certifications

There are several compelling reasons to take AI courses and certifications:

• Skill development: AI courses provide an opportunity to develop new skills and gain a deep understanding of artificial intelligence concepts.
• Career advancement: By acquiring AI skills and certifications, you can stand out, increase your employability, and access higher-paying job roles.
• Industry recognition: AI certifications serve as a recognized proof of your competency.
• Personal growth: AI courses challenge you to think critically, innovate, and approach problems from a different perspective.
• Market demand: The skills gained through AI courses and certifications are highly sought after by industries across sectors, including healthcare, finance, retail, manufacturing, and more. These courses can open up diverse job prospects and increase your job market value.

How to Choose the Right AI Certification Course

When researching the best AI certification course for you, you must consider certain factors unique to you. The best AI certification course for you will depend on your learning objectives, current field/industry knowledge, and learning budget. The list below can help you select the best course for you.

1. Learning objectives: Look for a course that covers the specific areas of AI that you are interested in or want to specialize in, such as machine learning, natural language processing, or computer vision. Also consider the depth and breadth of the course curriculum to ensure it aligns with your learning goals.
2. Course format and delivery: Consider your preferred mode of learning, whether it’s online, in-person classroom training, or a combination of both.
3. Instructor expertise: Check the qualifications, industry experience, and expertise of the course instructors.
4. Course resources: Consider the support and resources available during and after the course.
5. Cost: Evaluate the course fees and compare them with the value and benefits you will receive. Consider any financial aid or scholarships available.
6. Certification and career outcomes: Check if the course provides a recognized certification upon completion and consider the career outcomes and job placement records of the course graduates.

Remember that AI is a practical field, so seek courses that emphasize hands-on projects and real-world applications.

For more information, also see: Top Robotics Startups

Bottom Line: Which AI Certification is Right for You?

The right AI certification for you depends on your specific interests, career goals, and existing knowledge in the field. Truly finding the best course for you means evaluating the full range of AI training, but here are a few picks based on our analysis:

• UC Berkeley Artificial Intelligence: Business Strategies and Applications is best for business executives.
• Microsoft Certified: Azure AI Engineer Associate is ideal for developers interested in Azure AI services.
• AI for Everyone and Artificial Intelligence A-Z 2023: Build an AI with ChatGPT4 are best for beginners.

Read next: The AI Market: An Overview 

The post 20 Top Artificial Intelligence Certifications 2023 appeared first on eWEEK.

In short, Snowflake is more suited for standard data transformation and analysis and for those users familiar with SQL. Databricks is geared for streaming, ML, AI, and data science workloads courtesy of its Spark engine, which enables the use of multiple development languages.

Both Snowflake and Databricks provide the volume, speed, and quality demanded by business intelligence applications. But there are as many similarities as there are differences. When examined closely, it becomes clear that these two cloud-based data platforms have a different orientation. Therefore, selection often boils down to tool preference and suitability for the organization’s data strategy.

What Is Snowflake?

Snowflake is a major cloud company that focuses on data-as-a-service features and functions for big data operations. Its core platform is designed to seamlessly integrate data from various business apps and in different formats in a unified data store. Consequently, typical extract, transform, and load (ETL) operations may not be necessary to get the data integration results you need.

The platform is compatible with various types of business workloads, including artificial intelligence and machine learning, data lakes and data warehouses, and cybersecurity workloads. It is ideally designed for organizations that are working with large quantities of data that require precise data governance and management systems in place.

What Is Databricks?

Databricks is a data-driven vendor with products and services that focus on data lake and warehouse development as well as AI-driven analytics and automation. Its flagship lakehouse platform includes unified analytics and AI management features, data sharing and governance capabilities, AI and machine learning, and data warehousing and engineering.

Users can access certain platform features through an open-source format, making this a highly extensible and customizable solution for developers. It’s also a popular solution for users who want to incorporate other AI or IDE integrations into their setup.

Snowflake vs. Databricks: Comparing Key Features

We’ll compare these two data companies in greater detail in the sections to come, but for a quick scan, we’ve developed this table to compare Snowflake vs. Databricks across a few key metrics and categories:

Snowflake is a relational database management system and analytics data warehouse for structured and semi-structured data.

Offered via the software-as-a-service (SaaS) model, Snowflake uses an SQL database engine to manage how information is stored in the database. It can process queries against virtual warehouses within the overall warehouse, each one in its own cluster node independent of others so as not to share compute resources.

Sitting on top of that database engine are cloud services for authentication, infrastructure management, queries, and access controls. The Snowflake Elastic Data Warehouse enables users to analyze and store data utilizing Amazon S3 or Azure resources.

Databricks is also cloud-based but is based on Apache Spark. Its management layer is built around Apache Spark’s distributed computing framework to make infrastructure management easier. Databricks positions itself as a data lake rather than a data warehouse. Thus, the emphasis is more on use cases such as streaming, machine learning, and data science-based analytics.

Databricks can be used to handle raw unprocessed data in large volumes. Databricks is delivered as SaaS and can run on AWS, Azure, and Google Cloud. There is a data plane as well as a control plane for backend services that delivers instant compute. Its query engine is said to offer high performance via a caching layer. Snowflake includes a storage layer while Databricks provides storage by running on top of AWS S3, Azure Blob Storage, and Google Cloud Storage.

For those wanting a top-class data warehouse, Snowflake wins. But for those needing more robust ELT, data science, and machine learning features, Databricks is the winner.

Snowflake vs. Databricks: Support and Ease of Use Comparison

The Snowflake data warehouse is said to be user-friendly, with an intuitive SQL interface that makes it easy to get set up and running. It also has plenty of automation features to facilitate ease of use. Auto-scaling and auto-suspend, for example, help in stopping and starting clusters during idle or peak periods. Clusters can be resized easily.

Databricks, too, has auto-scaling for clusters. The UI is more complex for more arbitrary clusters and tools, but the Databricks SQL Warehouse uses a straightforward “t-shirt sizing approach” for clusters that makes it a user-friendly solution as well. 

Both tools emphasize ease of use in certain capacities, but Databricks is intended for a more technical audience, so certain steps like updating configurations and switching options may involve a steeper learning curve.

Both Snowflake and Databricks offer online, 24/7 support, and both have received high praise from customers in this area.

Though both are top players in this category, Snowflake wins for its wider range of user-friendly and democratized features.

Also see: Top Business Intelligence Software

Snowflake vs. Databricks: Security Comparison

Snowflake and Databricks both provide role-based access control (RBAC) and automatic encryption. Snowflake adds network isolation and other robust security features in tiers with each higher tier costing more. But on the plus side, you don’t end up paying for security features you don’t need or want.

Databricks, too, includes plenty of valuable security features. Both data vendors comply with SOC 2 Type II, ISO 27001, HIPAA, GDPR, and more.

No clear winner in this category.

Snowflake vs. Databricks: Integrations Comparison

Snowflake is on the AWS Marketplace but is not deeply embedded within the AWS ecosystem. In some cases, it can be challenging to pair Snowflake with other tools. But in other cases, Snowflake is wonderfully integrated. Apache Spark, IBM Cognos, Tableau, and Qlik are all fully integrated. Those using these tools will find analysis easy to accomplish.

Both tools support semi-structured and structured data. Databricks has more versatility in terms of supporting any format of data, including unstructured data. Snowflake is adding support for unstructured data now, too.

Databricks wins this category.

Also see: Top Data Mining Tools 

Snowflake vs. Databricks: AI Features Comparison

Both Snowflake and Databricks include a range of AI and AI-supported features in their portfolio, and the number only seems to grow as both vendors adopt generative AI and other advanced AI and ML capabilities.

Snowflake supports a range of AI and ML workloads, and in more recent years has added the following two AI-driven solutions to its portfolio: Snowpark and Streamlit. Snowpark offers users several libraries, runtimes, and APIs that are useful for ML and AI training as well as MLOps. Streamlit, now in public preview, can be used to build a variety of model types — including ML models — with Snowflake data and Python development best practices.

Databricks, on the other hand, has more heavily intertwined AI in all of its products and services and for a longer time. The platform includes highly accessible machine learning runtime clusters and frameworks, autoML for code generation, MLflow and a managed version of MLflow, model performance monitoring and AI governance, and tools to develop and manage generative AI and large language models.

While both vendors are making major strides in AI, Databricks takes the win here.

Snowflake vs. Databricks: Price Comparison

There is a great deal of difference in how these tools are priced. But speaking very generally: Databricks is priced at around $99 a month. There is also a free version. Snowflake works out at about $40 a month, though it isn’t as simple as that.

Snowflake keeps compute and storage separate in its pricing structure. And its pricing is complex with five different editions from basic up, and prices rise as you move up the tiers. Pricing will vary tremendously depending on the workload and the tier involved.

As storage is not included in its pricing, Databricks may work out cheaper for some users. It all depends on the way the storage is used and the frequency of use. Compute pricing for Databricks is also tiered and charged per unit of processing. The differences between them make it difficult to do a full apples-to-apples comparison. Users are advised to assess the resources they expect to need to support their forecast data volume, amount of processing, and their analysis requirements. For some users, Databricks will be cheaper, but for others, Snowflake will come out ahead.

This is a close one as it varies from use case to use case.

Also see: Real-Time Data Management Trends

Snowflake and Databricks Alternatives

Bottom Line: Snowflake vs. Databricks

Snowflake and Databricks are both excellent data platforms for data analysis purposes. Each has its pros and cons. Choosing the best platform for your business comes down to usage patterns, data volumes, workloads, and data strategies.

Snowflake is more suited for standard data transformation and analysis and for those users familiar with SQL. Databricks is more suited to streaming, ML, AI, and data science workloads courtesy of its Spark engine, which enables the use of multiple development languages. Snowflake has been playing catchup on languages and recently added support for Python, Java, and Scala.

Some say Snowflake is better for interactive queries as it optimizes storage at the time of ingestion. It also excels at handling BI workloads, and the production of reports and dashboards. As a data warehouse, it offers good performance. Some users note, though, that it struggles when faced with huge data volumes as would be found with streaming workloads. In a straight competition on data warehousing capabilities, Snowflake wins.

But Databricks isn’t really a data warehouse at all. Its data platform is wider in scope with better capabilities than Snowflake for ELT, data science, and machine learning. Users store data in managed object storage of their choice. It focuses on the data lake and data processing. But it is squarely aimed at data scientists and professional data analysts.

In summary, Databricks wins for a technical audience. Snowflake is highly accessible to a technical and less technical user base. Databricks provides pretty much every data management feature offered by Snowflake and a lot more. But it isn’t quite as easy to use, has a steeper learning curve, and requires more maintenance. Regardless though, Databricks can address a much wider set of data workloads and languages, and those familiar with Apache Spark will tend to gravitate toward Databricks.

Snowflake is better set up for users who want to deploy a good data warehouse and analytics tool rapidly without bogging down in configurations, data science minutia, or manual setup. But this isn’t to say that Snowflake is a light tool or for beginners. Far from it. 

But it isn’t high-end like Databricks, which is aimed more at complex data engineering, ETL, data science, and streaming workloads. Snowflake, in contrast, is a warehouse to store production data for analytics purposes. It is accessible for beginners, too, and for those who want to start small and scale up gradually.

Pricing comes into the selection picture, of course. Sometimes Databricks will be much cheaper due to the way it allows users to take care of their own storage. But not always. Sometimes Snowflake will pan out cheaper.

The post Snowflake vs. Databricks: Comparing Cloud Data Platforms appeared first on eWEEK.