Comparing MLOps Platforms

There is no shortage of MLOps platforms in the market today. In fact, once you’ve made the decision to acquire an MLOps capability, it’s very easy to be overwhelmed by the different options, complementary or overlapping features, and to understand whether or not a solution meets your needs. There are many lists, market guides, categorizations and more that attempt to document all the possible options, but it boils down to three types of MLOps platforms.

Cloud Native Platforms – each cloud service provider (CSP) includes native services for many components of the MLOps pipeline. This type of MLOps solution might be great for you if your organization already decided to operate within one (and only one) cloud ecosystem, and understands that there is not much room for customization. In addition, it can be incredibly costly to run models in the cloud, hence why some organizations are moving to on-prem options.¹

End-to-End Platforms – there are other solutions that offer comprehensive capabilities spanning the entire MLOps pipeline. An end-to-end solution might be right for you if your team is willing to adapt to the solution’s way of training and deploying production AI. If you’re a smaller company, a no-code or low-code solution might be just what you need to jumpstart your AI value creation. However, be prepared for potentially longer implementation and onboarding processes, and limited support for integrations that will ultimately keep you in the product’s ecosystem. Additionally, this type of MLOps platform can be more expensive because of all the functionality jam-packed into one solution, or lead to higher costs down the line if you end up wanting to swap in a better performing solution.

Best-of-Breed Platforms – Just like the DevOps market, the MLOps market is exploding with new best-of-breed solutions that offer increased flexibility and composability for your business and technology architectures. These types of MLOps platforms are generally designed specifically for one or more MLOps components and provide APIs that make creating integrations to other components intuitive and simple for today’s technologist. Depending on what your specific needs are, a best-of-breed solution could be the lower cost option for companies willing to accept a little more risk for access to the latest tech.

Features that Matter

As you explore different solution types, consider which factors are the most important for you to optimize for. Each organization will have its own unique criteria, but here are some of the most common factors to consider when evaluating the different types of MLOps platforms.

• Feature Complete. Does the solution provide the features your organization needs today and, in the future, (e.g., training, serving, monitoring, etc.)? Does it improve your key performance indicators (e.g., cycle time for training, cycle time for production deployment, infrastructure utilization, latency, etc.)?
• Ease of installation and deployment. Will it take days, weeks, or months to get the solution installed in your tech stack? Will the solution require extensive customizations to meet your needs? Can the solution accommodate your AI processing requirements (e.g., cloud, on-prem, disconnected, edge, hybrid)?
• Onboarding. How quickly can your users get up-to-speed on the new product – will they have to invest one hour or many hours to learn how to use the tool? Will your organizational processes have to adapt to the tool?
• Integration. How extensible is the solution and does it provide APIs/SDKs to support integrations with your data sources, training tools, front-end systems, etc.?
• Customer Support Resources. How quickly can you find what you’re looking for yourself or get in touch with customer support to ask a question or resolve an issue? What kind of resources are available to you? And, more importantly, does the support team value your time and treat you like a partner?
• Cost. Does the solution’s pricing model get in the way of your value stream? How does it support your current and future scaling needs? What other costs will you need to consider in your overall budget (e.g., labor, infrastructure to support model inference, SLAs for using open source)?

Getting Started

MLOps is an important component of your ML/AI tech stack, and making an informed choice can empower your teams, reduce costs, drive efficiencies, and accelerate your time to value. Building your business case, choosing the right vendor/partner, and putting together an implementation plan that will drive adoption and use are vital steps every team should take. Fortunately, there are many options, and using this criteria can help you identify the right type of MLOps platform for your team.

¹ Protocol, “Why AI and Machine Learning are Drifting Away from the Cloud.“