Azure Machine Learning documentation

Train and deploy machine learning models with Azure Machine Learning. Get started with quickstarts, explore tutorials, and manage your ML lifecycle with MLOps best practices.

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 reusable 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