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MLOps: Performing DevOps on Machine Learning Pilots

Now that organizations know what machine learning is, it is time to bring some discipline to the projects using MLOps. Though skeptical before, investors are now sponsoring more initiatives based on emerging technologies. To provide value to their interest, you must implement the lessons you have learned across the digital journey. In this article at InformationWeek, Jessica Davis throws light on how MLOps can pilot the machine learning initiatives.

Embracing MLOps

When you want to use your learnings effectively to new projects, MLOps helps apply them in proper stages. The approach is nothing but applying DevOps to machine learning projects. Omdia chief analyst Bradley Shimmin shares the best practices you must follow for MLOps.

How It Works

According to him, the more time you spend on these initiatives, the more you learn, and the more use cases you build for implementation. Moreover, Shimmin says, “You can’t really get that if you’re just focusing on one or two spot solutions like just trying to figure out turn rates or sales quarterly numbers.”

Targeting specific areas would not make your experience foolproof, though. So, you should have solutions for three machine learning requirements—‘repeatability, scalability, and surety’.

‘Repeatability means achieving the same results and being replicable. Scalability means you have enough processing power for the job you need to do. Surety means that you can trust the outcome and you can explain how the outcome was achieved.’

It is a collaborative effort with various roles crisscrossing during the process. MLOps is not just about aligning data with DevOps. It provides room for experimentation as well. Since data-related initiatives change demands frequently, it is hard to lock in on a particular framework. So, your MLOps platforms must have the capability of accommodating changes.

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