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Build, deploy, and manage machine learning applications on Azure Databricks. The integrated platform unifies the entire ML lifecycle from data preparation to production monitoring.
Get started
Try a quickstart, vibe code a model, and use notebooks.
| Guide | Description |
|---|---|
| Get started: Build your first machine learning model on Databricks | Build and deploy a simple classification model with scikit-learn. |
| Databricks notebooks | Collaborative development environment with support for Python, R, Scala, and SQL. |
| Concepts: Data science and machine learning on Azure Databricks | Learn the core concepts behind data science and machine learning on Azure Databricks. |
Train classic machine learning models
Engineer features, create machine learning models, and track experiments.
| Feature | Description |
|---|---|
| Feature Store | Do feature engineering, manage features in Unity Catalog, and serve features in production. |
| Model training examples | Explore end-to-end examples for training classic ML models with popular libraries. |
| Databricks Runtime for ML | Pre-configured clusters with scikit-learn, XGBoost, MLflow, and other ML libraries, plus support for deep learning frameworks. |
| MLflow tracking | Track experiments, compare model performance, and manage the complete model development lifecycle. |
Train deep learning models
Use managed compute and built-in frameworks to develop deep learning models.
| Feature | Description |
|---|---|
| Distributed training examples | Explore examples of distributed deep learning using Ray, TorchDistributor, and DeepSpeed. |
| DL best practices | Learn about framework choice, data loading, distributed scaling, and managing the deep learning model lifecycle. |
| Ray on Databricks | Scale ML workloads with distributed computing for large-scale model training and inference. |
Deploy and serve models
Deploy models to production with scalable endpoints for real-time, streaming, or batch inference.
Monitor and govern ML systems
Ensure model quality, data integrity, and compliance with comprehensive monitoring and governance tools.
| Feature | Description |
|---|---|
| Unity Catalog | Govern data, features, models, and functions with unified access control, lineage tracking, and discovery. |
| MLflow for Models | Manage the full ML lifecycle, from experiments and models to evaluation and deployment. |
Productionize ML workflows
Scale machine learning operations with automated workflows, CI/CD integration, and production-ready pipelines.
| Feature | Description |
|---|---|
| Models in Unity Catalog | Use the model registry in Unity Catalog for centralized governance and to manage the model lifecycle, including deployments. |
| Lakeflow Jobs | Build automated workflows for ML pipelines. |
| Declarative Automation Bundles | Manage Azure Databricks infrastructure as code for CI/CD, including ML training and deployment. |
| MLOps workflows | Learn about end-to-end MLOps with automated training, testing, and deployment pipelines. |