What are MLOps and Why Does it Matter?


Courtesy OSDC-Open Data Science

MLOps is a framework for collaboration between data scientists and the operations or production team. It is designed to eliminate waste, reduce errors, automate as much as possible, and produce richer, more consistent insights with machine learning.

MLOps follows a similar pattern to DevOps. The practices that drive a seamless integration between your development cycle and your overall operations process can also transform how your organization handles big data. Just like DevOps shortens production life cycles by creating better products with each iteration, MLOps drives insights you can trust and put into play more quickly.

So How Do We Introduce MLOps?

As per recommendations of OSDC-Open Data Science and other ML experts, consider few things before you build MLOps into your applications. Bringing ML into production does require your organization to put in place processes and procedures on a few things before your model can officially be known as MLOps.

  • What are the benchmarks? — Your KPIs should be clear and measurable so that everyone is on board. Data science teams understand what’s at stake and operations personnel understand how to use insights to move forward or pivot.
  • Who is monitoring? — ML uses non-intuitive mathematical functions. The black box requires constant monitoring to ensure your ML continues to deliver expected performance, operating within regulation, and that programs are returning quality information.
  • Periodic Iterations  and Updates— You may have to retrain data periodically, and determining how and when to do so needs critical collaboration between the teams involved. With an operational system in place, there shouldn’t be any confusion.
  • Release Management— Put in place process for ML release  management with defined responsibilities of data scientists and operations. Figure out, how the models will be rolled out all the consuming applications and endpoints which may be in the cloud or on the edge?
  • Regulations— MLOps should have a comprehensive plan for governance to ensure your programs are auditable and to assist with explainability.

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