Building machine learning models are hard, but, getting machine learning into production is even harder! This is where Machine Learning Operations (MLOps) comes to the rescue. MLOps—a term derived from machine learning or ML and operations or Ops—is a set of processes designed to deploy and maintain ML models efficiently and reliably in production.

There are many opinionated MLOps tools and platforms available, as you will immediately discover if you conduct a short online search. There are numerous end-to-end machine learning platforms, life cycle management tools, model deployment and serving tools, monitoring and model drift tools, and feature stores available to choose from. It can be extremely challenging for even an experienced machine learning engineer, let alone someone with little MLOps experience, to figure out how to use all these tools to do MLOps well.

The goal here is to leverage MLOps tools available in Azure cloud platform to enable data scientists and machine learning engineers to collaborate effectively and increase the pace of model development and production.