Comparing MLOps platforms can help you identify which type has the right features for your unique needs.
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.
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.
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.