Azure Machine Learning documentation

Learn how to train and deploy models and manage the ML lifecycle (MLOps) with Azure Machine Learning. Tutorials, code examples, API references, and more.

Overview

Overview

• What is Azure Machine Learning?
• What is Responsible AI?

Setup & quickstart

Quickstart

• Create resources
• Get started with Azure Machine Learning

Start with the basics

Tutorial

• Prepare and explore data
• Develop on a cloud workstation
• Train a model
• Deploy a model
• Set up a reusable pipeline

Build AI solutions

Quickstart

• What is Azure Machine Learning prompt flow?
• Get started in prompt flow

Work with data

How-To Guide

• Use Apache Spark in Azure Machine Learning
• Create data assets

Train models

How-To Guide

• Run training with CLI, SDK, or REST API
• Build pipelines from reuseable components

Deploy models

Deploy

• Streamline model deployment with endpoints
• Real-time scoring with online endpoints

Manage the ML lifecycle (MLOps)

How-To Guide

• Track, monitor, analyze training runs
• Model management, deployment & monitoring

Security for ML projects

How-To Guide

• Create a secure workspace
• Connect to data sources
• Enterprise security & governance

Reference docs

Reference

• Python SDK (v2)
• CLI (v2)
• REST API
• Algorithm & component reference

Resources

Reference

• Upgrade to v2
• Python SDK (v2) code examples
• CLI (v2) code examples