Getting Started with MLflow

Efficiently delivering machine learning products is not easy, therefore good tools that support ML model development are needed. This course will teach you MLflow.

Developing machine learning models in teams, with real-world data and serving real-world business needs may be complex. In this course, Getting Started with MLflow, you’ll learn to manage the full lifecycle of machine learning models. First, you’ll explore how to track your machine learning experiments for easy comparison and reproducibility. Next, you’ll discover ways of using MLflow to collaborate on model development in teams of any size. Finally, you’ll learn how to share your models in a way that makes them ready for use in real products. When you’re finished with this course, you’ll have the skills and knowledge of MLflow needed to create machine learning models in a collaborative, reproducible, and production-ready way.

• Course Overview 1min
• Understanding MLflow 11mins
• Tracking ML Experiments 14mins
• Exporting Artifacts 10mins
• Using MLflow in a Collaborative Scenario 14mins
• Packaging and Running Models 17mins
• Sharing and Managing Models with Model Registry 9mins
• Summary 3mins