Databricks Machine Learning guide
Databricks Machine Learning is an integrated end-to-end machine learning environment incorporating managed services for experiment tracking, model training, feature development and management, and feature and model serving. The diagram shows how the capabilities of Azure Databricks map to the steps of the model development and deployment process.
Databricks Machine Learning overview
With Databricks Machine Learning, you can:
- Train models either manually or with AutoML
- Track training parameters and models using experiments with MLflow tracking
- Create feature tables and access them for model training and inference
- Share, manage, and serve models using Model Registry
You also have access to all of the capabilities of the Azure Databricks workspace, such as notebooks, clusters, jobs, data, Delta tables, security and admin controls, and so on. For more information, see the Databricks Data Science & Engineering guide.
For machine learning applications, Databricks recommends using a cluster running Databricks Runtime for Machine Learning.
To get started, move your mouse or pointer over the left sidebar in the Azure Databricks workspace. The sidebar expands as you mouse over it. From the persona switcher at the top of the sidebar, select Machine Learning. The Databricks Machine Learning home page appears.
For more information about using the sidebar, see Use the sidebar.
- Databricks Machine Learning home page
- Prepare data
- Environment setup
- Databricks AutoML
- Train models
- Track model development
- Manage models
- Deploy models
- Export and import models
- Reference solutions
- MLflow guide