MLOps with MLflow: Registering & Deploying ML Models

The MLflow Model Registry enables easy registration and deployment of machine learning (ML) models for future use either locally or in the cloud. It streamlines model management facilitating collaboration among team members during model development and deployment. In this course you will create classification models using the regular ML workflow. You ll see that visualizing and cleaning data running experiments and analyzing model performance using SHapley Additive exPlanations (SHAP) will provide valuable insights for decision-making. You ll also discover how programmatic comparison will AId in selecting the best-performing model. Next you ll explore the powerful MLflow Models feature enabling efficient model versioning and management. You ll learn how to modify registered model versions work with different versions of the same model and serve models to Representational State Transfer (REST) endpoints. Finally you ll explore integrating MLflow with Azure Machine Learning leveraging the cloud s power for model development.