Advanced

Machine Learning with Azure Databricks

The Databricks ML lifecycle: MLflow tracking, tuning with Ray, the model registry, serving and AutoML.

Level: Intermediate / Advanced | Prerequisites: Databricks Premium Workspace

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What's inside

21 sections
  1. 1 Table of Contents
  2. 2 Why Databricks for Machine Learning?
  3. 3 Databricks ML Runtime
  4. 4 MLflow — ML Lifecycle Management Platform
  5. 5 Exploratory Data Analysis (EDA)
  6. 6 Data Preprocessing and Preparation
  7. 7 Training Models with scikit-learn
  8. 8 MLflow Tracking — Experiment Tracking
  9. 9 MLflow Autologging
  10. 10 Hyperparameter Tuning with Ray Tune
  11. 11 Model Registry with Unity Catalog
  12. 12 Model Serving — Deploying as REST API
  13. 13 Predictions from a Deployed Model
  14. 14 Automated Model Retraining
  15. 15 Databricks AutoML
  16. 16 ML Orchestration with Azure Data Factory
  17. 17 Databricks Feature Store
  18. 18 Distributed Machine Learning with Spark MLlib
  19. 19 MLOps Best Practices on Databricks
  20. 20 Summary and Tool Comparison
  21. 21 Glossary

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