dagshub-docs/docs/workshops/mlflow_crash_course.md

MLflow Crash Course

With 243M downloads and over 13K stars on GitHub - MLflow{:rel="nofollow" target="_blank"} is one of the most widely adopted open-source tools for machine learning lifecycle management.

In this two part course, we teach you how to get started with MLflow and use it to track experiments, register models, and deploy them to AWS.

What is MLflow & MLflow Tracking?

The first course in our series will give you an introduction to MLflow. You will:

• Learn what MLflow is and how it can help you manage your machine learning project
• Use experiment tracking to log parameters, metrics, and artifacts live as part of a machine learning pipeline

Materials

• MLflow Crash Course Part 1 Colab{:rel="nofollow" target="_blank"}
• Mario vs Wario Sample Project{target="_blank"}

Model Registry & Model Deployment

The second course covers some advanced features of MLflow. In this workshop, you will:

• Learn how to log and manage your machine learning models to the Model Registry
• Deploy your trained model from the Model Registry to Amazon Web Services (AWS)

Materials

• MLflow Crash Course Part 2 Colab{:rel="nofollow" target="_blank"}
• Mario vs Wario Sample Project{target="_blank"}