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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
  • Previous Versions
  • Blog
  • Contribute
  • Privacy
  • Terms of Use
  • Trademarks
  • © Microsoft 2023
  • Previous Versions
  • Blog
  • Contribute
  • Privacy
  • Terms of Use
  • Trademarks
  • © Microsoft 2023