Azure Machine Learning Service Documentation
Azure Machine Learning service provides SDKs and services to quickly prep data, train, and deploy machine learning models. Improve productivity and costs with autoscaling compute & pipelines. Use these capabilities with open-source Python frameworks, such as PyTorch, TensorFlow, and scikit-learn. Get started with our quickstarts and tutorials.
After you create a workspace for Azure Machine Learning service, you can get started using these quickstarts:
- Run an experiment in the cloud (no install required).
- Run an experiment using a local notebook server (local SDK install).
- Prepare and visualize data without writing code in the visual interface.
Learn how to prep data, set up experiments, train and operationalize your machine learning models:
- Train and deploy image classification models (two-part series).
- Prep data and use automated machine learning to train & tune regression models (two-part series).
- Predict automobile price with the visual interface and deploy the regression model.
Find Jupyter notebooks to help you explore the service on Github.
- Main Python SDK: core functionality, automated machine learning, pipelines, and more
- Monitoring Python SDK: collect data on deployed web services
- Algorithm & module reference for the visual interface
- Azure ML CLI: a machine learning command-line interface extension