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.
Learn how to prep data, set up experiments, train and operationalize your machine learning models.
Use the Azure portal, an easy-to-use, no code interface for machine learning:
- Train your first automated machine learning experiment.
- Train your first experiment in visual interface"s drag and drop UI.
Use the Python SDK in scripts and notebooks for machine learning:
- Set up your environment & train your first ML experiment.
- Train & deploy image classification models.
- Prepare data and use automated ML to predict taxi fares.
- Run batch predictions on large data sets with ML pipelines.
Find Jupyter notebooks to help you explore the service on Github.
- Main Python SDK docs: learn about core functionality, automated ML, pipelines, and more.
- Monitoring Python SDK docs: learn how to collect data on deployed web services
- Algorithm & module reference docs for the visual interface modules.
- Azure ML CLI: learn about the ML command-line interface extension.