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