Getting Started with Azure Machine Learning

Getting Started with Azure Machine Learning

• 1.
  Azure Machine Learning
• 2.
  Eng Teong Cheah MicrosoftMVP
• 3.
  Azure Machine Learning AzureMachine 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 helps you monitor, retrain, and redeploy models.
• 4.
  Who is AzureMachine Learning for? Azure Machine Learning is for individuals and teams implementing MLOps within their organization to bring machine learning models into production in a secure and auditable production environment. Data scientists and ML engineers will find tools to accelerate and automate their day-to- day workflows. Application developers will find tools for integrating models into applications or services. Platform developers will find a robust set of tools, backed by durable Azure Resource Manager APIs, for building advanced ML tooling. Enterprises working in the Microsoft Azure cloud will find familiar security and role-based access control (RBAC) for infrastructure. You can set up a project to deny access to protected data and select operations.
• 5.
  MLOps: DevOps formachine learning DevOps for machine learning models, often called MLOps, is a process for developing models for production. A model's lifecycle from training to deployment must be auditable if not reproducible.
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  Workspaces The workspace isthe top-level resource for Azure Machine Learning, providing a centralized place to work with all the artifacts you create when you use Azure Machine Learning. The workspace keeps a history of all jobs, including logs, metrics, output, and a snapshot of your scripts. The workspace stores references to resources like datastores and compute. It also holds all assets like models, environments, components and data asset.
• 7.
  Demo Create Machine Learning Workspace
• 8.
  References Microsoft Docs