Managing the Complete Machine Learning Lifecycle with MLflow - Introduction to MLflow Tracking

Discover how to manage the complete machine learning lifecycle using MLflow in this beginner to intermediate-level workshop. Learn about MLflow Tracking to record and query experiments, MLflow Projects for reproducible runs, MLflow Models for diverse deployment, and Model Registry for collaborative lifecycle management. Explore the MLflow UI to visually compare experimental runs and evaluate metrics. Gain hands-on experience with practical examples and prepare to enhance your machine learning development process. This 58-minute session is the first in a three-part series, focusing on an introduction to MLflow and its key components.

Intro
Complexity
Traditional vs ML
The Problem
GroundUp API
Tracking
Emma for Tracking
Summary
Community
Tutorials
Creating a Cluster
Using Notebooks
Running Existing Notebooks
Python Classes
Running the Experiment
MLflow UI
Questions