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?
• Glossary
• What is CLI & SDK v2?

Get Started

Tutorial

• Set up resources
• Run Jupyter notebooks
• Azure Machine Learning in a day

Work with data

How-To Guide

• Create datastore
• Access data in a notebook
• Use Apache Spark in Azure Machine Learning
• Create data assets
• Work with tables

Train models

How-To Guide

• Run training with CLI, SDK, or REST API
• Tune hyperparameters for model training
• Build pipelines from reuseable components
• Use automated ML in studio
• Use designer (drag-n-drop) in studio
• Train with R

Deploy models

Deploy

• Streamline model deployment with endpoints
• Real-time scoring with online endpoints
• Batch scoring with batch endpoints
• Deploy R models

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
• ML Studio (classic) documentation