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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?
  • What is Azure Machine Learning studio?
  • Key concepts (v2)
  • What is CLI & SDK v2?

Get Started

Tutorial

  • Set up resources
  • Train with the v2 CLI
  • Train with the v2 SDK (preview)
  • Day 1: Get started with Python
  • Migrate from ML Studio (classic)

Train models

How-To Guide

  • Run training code in the cloud (v2 CLI)
  • Train and deploy a model in Jupyter notebook
  • Tune hyperparameters for model training
  • Build pipelines from reuseable components
  • AutoML: Train a classification model

Deploy models

Deploy

  • Streamline model deployment with endpoints (preview)
  • Real-time scoring with a managed endpoint (preview)
  • Batch scoring with managed endpoints (preview)
  • Options for deploying models without using endpoints

Manage the ML lifecycle (MLOps)

How-To Guide

  • Track, monitor, analyze training runs
  • Model management, deployment, lineage & monitoring
  • DevOps for a data ingestion pipeline

Security for ML projects

How-To Guide

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

Reference docs

Reference

  • Python SDK
  • REST API
  • V2 CLI (preview)
  • Algorithm & component reference

Resources

Reference

  • Python SDK code examples
  • v2 CLI code examples (preview)
  • ML Studio (classic) documentation
  • Previous Version Docs
  • Blog
  • Contribute
  • Privacy & Cookies
  • Terms of Use
  • Trademarks
  • © Microsoft 2022
  • Previous Version Docs
  • Blog
  • Contribute
  • Privacy & Cookies
  • Terms of Use
  • Trademarks
  • © Microsoft 2022