Design and Provision AML Infrastructure

Azure Machine Learning is a cloud service for accelerating and managing the machine learning project lifecycle. Machine learning professionals, data scientists, and engineers can use it in their day-to-day workflows: Train and deploy models, and manage MLOps.

You can create a model in Azure Machine Learning or use a model built from an open-source platform, such as Pytorch, TensorFlow, or scikit-learn. MLOps tools help you monitor, retrain, and redeploy models. Before deployment with Azure resources, development teams should:

  1. Understand the architecture and design concepts of Azure Machine Learning.

  2. Learn about model management, deployment, lineage and monitoring with Azure Machine Learning.

  3. Understand the technology choices available.

  4. Understand the technology Selection criteria for edge deployment.

  5. Understand the Enterprise security and governance for Azure Machine Learning

  6. Set up authentication for Azure Machine Learning resources and workflows

  7. Manage access to an Azure Machine Learning workspace

  8. Understand cost management of the Azure Machine Learning services

Deliverables

GET STARTED: Start by understanding the architecture and design concepts of Azure Machine Learning here.