Book Content
chapters • 8h16m total length
1. Introducing MLflow
2. Your Machine Learning Project
3. Your Data Science Workbench
4. Experiment Management in MLflow
5. Managing Models with MLflow
6. Introducing ML Systems Architecture
7. Data and Feature Management
8. Training Models with MLflow
9. Deployment and Inference with MLflow
10. Scaling Up Your Machine Learning Workflow
11. Performance Monitoring
12. Advanced Topics with MLflow
