Managing the end-to-end lifecycle of a Machine Learning application can be a daunting task for data scientists, engineers, and developers. Machine Learning applications are complex and have a proven track record of being difficult to track, hard to reproduce, and problematic to deploy.

In this course, you will learn what MLflow is and how it attempts to simplify the difficulties of the Machine Learning lifecycle such as tracking, reproducibility, and deployment. After learning MLflow, you will have a better understanding of how to overcome the complexities of building Machine Learning applications and how to navigate different stages of the Machine Learning lifecycle.

Throughout the course, you will deep dive into the four major components that make up the MLflow platform. You will explore how to track models, metrics, and parameters with MLflow Tracking, package reproducible ML code using MLflow Projects, create and deploy models using MLflow Models, and store and version control models using Model Registry.

As you progress through the course, you will also learn best practices of using MLflow for versioning models, how to evaluate models, add customizations to models, and how to build automation into training runs. This course will prepare you for success in managing the lifecycle of your next Machine Learning application.