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Machine learning operations, better known as MLOps, is a strategic approach to machine learning model development that aims to standardize and make repeatable the machine learning model creation process and lifecycle.
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.
TABLE OF CONTENTS
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:
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.