Learn how to use Azure Machine Learning services for experimentation and model management.
As a pre-requisite, run the configuration Notebook notebook first to set up your Azure ML Workspace. Then, run the notebooks in following recommended order.
- train-within-notebook: Train a model hile tracking run history, and learn how to deploy the model as web service to Azure Container Instance.
- train-on-local: Learn how to submit a run to local computer and use Azure ML managed run configuration.
- train-on-amlcompute: Use a 1-n node Azure ML managed compute cluster for remote runs on Azure CPU or GPU infrastructure.
- train-on-remote-vm: Use Data Science Virtual Machine as a target for remote runs.
- logging-api: Learn about the details of logging metrics to run history.
- register-model-create-image-deploy-service: Learn about the details of model management.
- production-deploy-to-aks Deploy a model to production at scale on Azure Kubernetes Service.
- enable-data-collection-for-models-in-aks Learn about data collection APIs for deployed model.
- enable-app-insights-in-production-service Learn how to use App Insights with production web service.
Find quickstarts, end-to-end tutorials, and how-tos on the official documentation site for Azure Machine Learning service.
