MLOps is a set of practices used to maintain, deploy and manage machine learning models in production.

In a nutshell, MLOps engineer work started once the build and tested.

The MLOps engineer role is relatively new and its demand is growing day by day.

MLOps engineer evaluates the model performance once the machine learning model is deployed in production.

MLOps engineers should be aware of DevOps, machine learning, and data engineering technologies.

MLOps engineers also need to perform model governance, model Monitoring, and model versioning.

MLOps engineers should be aware of tools like MLFlow and kubeflow which are used to manage the life cycle of machine learning models.