This article provides an overview of Databricks Machine Learning including guidance on how to access the Databricks Machine Learning UI and links to tutorials and user guides for common ML tasks and scenarios.
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:
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 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, see:
To learn about key Databricks Machine Learning features, see:
For a recommended MLOps workflow on Databricks Machine Learning, see:
To learn more about using Databricks Machine Learning for the typical steps in developing and deploying a machine learning model, see: