Azure ML Workspace Fundamentals
Azure ML workspace architecture, compute, datasets, environments, governance, security and cost.
Imagine opening a large restaurant. You need a kitchen, a pantry, recipes, and chefs. Instead of managing everything from separate buildings, you create one central restaurant where everything is organized together.
That is exactly what an Azure ML Workspace is: the central headquarters for all your machine learning projects.
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What's inside
20 sections- 1 Table of Contents
- 2 Azure ML Workspace Overview
- 3 Workspace Architecture and Dependencies
- 4 Creating and Configuring a Workspace
- 5 Compute – Compute Infrastructure
- 6 Data and Datasets
- 7 Environments – Reproducibility
- 8 Governance and Lifecycle
- 9 Workspace Security
- 10 Practical Implementation with the SDK
- 11 Cost Optimization
- 12 Summary and Key Points
- 13 Glossary
- 14 Table of Contents
- 15 Azure ML Workspace Overview
- 16 Compute – Compute Infrastructure
- 17 Data and Datasets
- 18 Environments – Reproducible Environments
- 19 Governance and Lifecycle
- 20 Summary and Key Points
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