Welcome to Azure Machine Learning

Azure Machine Learning is a cloud-based environment that you can use to train, deploy, automate, manage, and track machine learning models. It is designed to accelerate your end-to-end machine learning lifecycle.

Whether you are a seasoned data scientist or just beginning your journey, Azure ML provides the tools and services to build powerful AI solutions. Explore the resources below to get started.

Getting Started Quickly

Set up Your Workspace

Learn how to create and configure your Azure Machine Learning workspace, the central hub for all your ML activities.

Create Workspace

First ML Project

Follow a step-by-step guide to build and train your first machine learning model using Azure ML.

Start Project

Explore SDKs

Discover the Azure Machine Learning SDKs for Python and R, enabling seamless integration with your preferred development environments.

Explore SDKs

Hands-on Tutorials

Image Classification

Train a deep learning model for image classification using Azure ML's managed compute and automated ML.

View Tutorial

Text Analysis

Build natural language processing models for sentiment analysis or text summarization.

View Tutorial

Time Series Forecasting

Implement forecasting models for predicting future trends with time-series data.

View Tutorial

Reinforcement Learning

Explore advanced topics like reinforcement learning with curated examples.

View Tutorial

Key Concepts

Understand the fundamental building blocks of Azure Machine Learning:

  • Workspaces: The foundational resource for managing your ML projects.
  • Compute Resources: Various compute targets for training and deployment, from CPUs to GPUs.
  • Datastores: Securely connect to and manage your data sources.
  • Experiments & Runs: Track and compare your model training iterations.
  • Models: Register, version, and manage your trained models.
  • Endpoints: Deploy models for real-time inference or batch scoring.

Comprehensive Documentation

Azure ML SDK (Python)

In-depth API references, usage examples, and best practices for the Python SDK.

Python SDK Docs

Azure ML CLI (v2)

Learn to manage your Azure ML resources and workflows using the command-line interface.

CLI Docs

Azure ML Studio

Navigate the visual interface for data preparation, model training, and deployment.

Studio Guide

Best Practices & Architecture

Learn how to build scalable, robust, and responsible AI solutions on Azure:

  • Architectures for ML solutions
  • MLOps: CI/CD for machine learning
  • Responsible AI principles
  • Security and compliance

Community & Support

Connect with other Azure ML users, find answers, and contribute: