Intermediate

Azure ML: Practical Use Cases

Choose the right technique and run classification, clustering and batch inference with AutoML and the Designer.

Imagine your manager arrives with: "We need an ML solution for fraud detection." Or: "Can we predict next quarter's sales?" Or: "Segment our customers into coherent groups."

The classic problem: Managers don't speak in ML terms. They formulate business problems. If you can't transform a business problem into the appropriate ML type, you'll waste weeks on the wrong approach.

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What's inside

14 sections
  1. 1 Table of Contents
  2. 2 Introduction to ML Use Cases
  3. 3 Choosing the Right ML Technique
  4. 4 AutoML vs Designer – Decision Guide
  5. 5 Classification with AutoML – Complete Guide
  6. 6 Clustering and Segmentation with Designer
  7. 7 End-to-End Workflows with the Designer
  8. 8 Model Evaluation and Metrics
  9. 9 Batch Inferencing – Operationalization
  10. 10 Implementation with Python SDK v2
  11. 11 Model Deployment and Consumption
  12. 12 Best Practices and Patterns
  13. 13 Summary and Key Points
  14. 14 Glossary

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