Introduction to Databricks Machine Learning
This article is an introduction to Databricks Machine Learning. It describes the benefits of using Databricks for common ML tasks and provides links to notebooks, tutorials, and user guides to help you get started.
What is Databricks Machine Learning?
Databricks Machine Learning provides an integrated machine learning environment that helps you simplify and standardize your ML development processes. With Databricks Machine Learning, you can:
Track training parameters and model performance using experiments with MLflow tracking.
Share, manage, and serve models using Model Registry and Databricks Model Serving.
Use the Databricks Feature Store to develop and share features, track upstream and downstream feature lineage, and serve feature values online.
Databricks Machine Learning also includes all of the capabilities of the Databricks workspace, including:
Data exploration, management, and governance.
Code management with Databricks Repos.
Automation support including Delta Live Tables, Databricks Jobs, and APIs.
For machine learning applications, Databricks recommends using a cluster running Introduction to Databricks Runtime for Machine Learning.
Use Databricks for model development and deployment
The diagram shows how the capabilities of Databricks map to the steps of the model development and deployment process.
To get started, see:
To learn about key Databricks Machine Learning features, see:
For a recommended MLOps workflow on Databricks Machine Learning, see: