The Value of MLOps for Operationalization of AI Solutions Beyond a Single Use Case
You can deploy a single AI/ML solution in production utilizing SaaS and PaaS from major cloud platforms. But to realize value at the Enterprise, you need MLOps to scale/repeat it for other use cases.
In a previous article I spoke about deploying AI/ML solutions rapidly on Serverless ML, so that AI-naive Enterprises can evaluate the value of shifting from AI prototypes to an operational AI/ML system. So now that you deployed your first AI/ML solution in production for a very specific use case, how do you repeat this and scale this to other use cases? The answer to this need to scale for other use cases is MLOps.
Let’s say phase 1 of this exploratory exercise was to show the value of deploying an AI/ML solution in production for a specific use case and need in your organization. Now that you completed this and show the value, then the executives want more of it, and want to see how this operational AI/ML solution can be applied to other AI prototypes in the organization, waiting for an Enterprise AI/ML platform on which to deploy and operationalize their own AI/ML solutions. You can reuse and scale this for other use cases with MLOps.
So what’s the big deal with MLOps? You say you can already deploy your first AI/ML use case in production, so why is there a need for another process/framework? These are valid questions, and here I attempt to convince you why you will need MLOps going forward, on your journey to maturing Enterprise Data Science capabilities at your organization.
MLOps provides the following additional capabilities for Enterprise Data Science in your AI-naive organization:
Have a repeatable process to scale the AI/ML solution you operationalized in phase 1 (for a very specific use case), to include other AI/ML use cases that other areas want to operationalize in the organization.
Implement a seamless, automated and continuous process to deploy all AI/ML models in production (CI/CD- Continuous Integration and Continuous Delivery).
Implement a structured and efficient process to monitor the AI/ML model in production, accounting for data drift and concept drift, and when the AI/ML model becomes inaccurate, to have an efficient process to re-train the AI/ML model to get back the accuracy, which always degrades over time. This is called CM/CT- Continuous Monitoring and Continuous Training.
When you take the above together, this forms the framework and pipelines required for an MLOps platform to be built in the next phase. Technically, this MLOps will incorporate CI/CD/CM/CT. Your AI/ML teams should work with the major cloud platforms for SaaS and PaaS with Serverless ML, and work with the solutions and platform architects to design and build such MLOps platform for the 2nd phase, to scale your operationalized AI/ML solution to other use cases.
In future articles, I will show you how to implement such MLOps platform for operationalizing your other use cases.
If you're looking for support from me, here are a few options:
Enterprise Data Science Consultancy: With my consult team comprised of a Senior Data Scientist, Senior ML Engineer, Senior Data Engineer, and Senior Cloud Engineer, we will help you architect and build your Enterprise Data Science platform, and transfer knowledge to your IT team to maintain and optimize it. We will also overlay an MLOps framework to manage the AI solutions you build on this platform. If you don’t have an MLOps team, we will help you build one. Please get in touch about this consultancy here
Coaching and Mentorship: I offer coaching and mentorship; book a coaching session here