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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, prepare your data, or build a low-code model.
| Guide | Description |
|---|---|
| Get started: Build your first machine learning model on Databricks | Build a simple classification model with scikit-learn end-to-end. |
| AutoML | Automatically build high-quality models with minimal code using automated feature engineering and hyperparameter tuning. |
| Load data for machine learning and deep learning | Load and prepare data for ML and deep learning workflows. |
| Train recommender models | Train a recommender model with the two-tower or DLRM architecture. |
Train classic machine learning models
Create machine learning models with automated tools and collaborative development environments.
| Feature | Description |
|---|---|
| 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. |
| Feature engineering | Create, manage, and serve features with automated data pipelines and feature discovery. |
| Databricks notebooks | Collaborative development environment with support for Python, R, Scala, and SQL for ML workflows. |
Train deep learning models
Use managed compute and built-in frameworks to develop deep learning models.
| Feature | Description |
|---|---|
| Distributed training | Examples of distributed deep learning using Ray, TorchDistributor, and DeepSpeed. |
| DL best practices | Guidance for framework choice, data loading, distributed scaling, and managing the deep learning model lifecycle. |
| PyTorch | Single-node and distributed training using PyTorch. |
Deploy and serve models
Deploy models to production with scalable endpoints, real-time inference, and enterprise-grade monitoring.
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 | Track, evaluate, and monitor generative AI applications throughout the development lifecycle. |
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 and production-ready ETL pipelines for ML data processing. |
| Ray on Databricks | Scale ML workloads with distributed computing for large-scale model training and inference. |
| MLOps workflows | Implement end-to-end MLOps with automated training, testing, and deployment pipelines. |