Databricks Machine Learning guide

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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 Databricks map to the steps of the model development and deployment process.

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

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 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.

For general information about the tools available to you in the Databricks workspace, such as notebooks, clusters, jobs, data, Delta tables, and so on, see the Databricks Data Science & Engineering guide.

Tutorials

• Quickstart
• 10-minute tutorials

User guides

• Databricks Machine Learning home page
• Prepare data
• Environment setup
• Databricks AutoML
• Train models
• Track model development
• Inference
• Productionize models
• Deploy models
• Export and import models
• Reference solutions
• MLflow guide

Feature guides

• Databricks Feature Store
• Experiments
• Models

• Databricks Runtime for Machine Learning