Working with MLOps in Azure Machine Learning

The term MLOps is a combination of Machine Learning and Operations and refers to the practices, tools, and strategies for the life cycle management of ML models in a production environment. Just as DevOps aims to streamline the development and operations processes for software, MLOps aims to do the same for ML systems. Implementing MLOps can improve productivity, reproducibility, and agility in ML projects. MLOps focuses on a specific set of practices.

Let us explore each practice and how we can use Azure Machine Learning features for each one:

• Collaboration: Facilitating effective collaboration between various roles such as data scientists, ML engineers, and operations teams is core in ML projects as there are multiple roles involved in the success of the project. By using shared platforms and tools, a data scientist focuses on model prototyping, an ML engineer ensures it’s production-ready, and an operations specialist...