Learn how to use Azure Machine Learning services for experimentation and model management.
If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, go through the configuration Notebook first if you haven't already to establish your connection to the AzureML 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.
