Lilly Tech Systems
Learn MLflow
Master the open-source ML lifecycle platform — from experiment tracking and model packaging to the model registry and production deployment.
Your Learning Path
Follow these lessons in order, or jump to any topic that interests you.
1. Introduction
What is MLflow? Its four components, why it matters, and how it compares to alternatives.
2. Installation & Setup
Install MLflow, start the tracking server, configure backends, and run your first experiment.
3. Tracking
Log parameters, metrics, and artifacts. Use autologging and compare runs in the UI.
4. Projects
Package ML code for reproducible runs with MLproject files, environments, and entry points.
5. Models
Model flavors, signatures, logging, loading, and custom model wrappers.
6. Model Registry
Register models, manage versions, stage transitions, and approval workflows.
7. Deployment
Serve models via REST API, Docker, Kubernetes, and cloud platforms.
8. Best Practices
Experiment organization, naming conventions, team collaboration, and CI/CD integration.
What You'll Learn
By the end of this course, you'll be able to:
Track Experiments
Log every parameter, metric, and artifact from your ML experiments for full reproducibility.
Package Models
Use MLflow Models format to package models from any framework for portable deployment.
Manage Model Lifecycle
Use the Model Registry to version, stage, and promote models through your workflow.
Deploy Anywhere
Serve models as REST APIs, in Docker containers, on Kubernetes, or in the cloud.