Deploying Models with Azure Machine Learning
Online and batch endpoints, scoring scripts, blue/green deployment, AKS and model monitoring on Azure ML.
Training an ML model is only half the work. Production deployment is often the most complex and risky step in the ML lifecycle.
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What's inside
20 sections- 1 Table of Contents
- 2 Introduction to ML Deployment
- 3 Model Catalog and Fine-tuning
- 4 Online Endpoints – Real-time Inference
- 5 Scoring Script – The Core of Deployment
- 6 Blue/Green Deployment and Traffic Splitting
- 7 Batch Endpoints – Batch Inference
- 8 Deployment on AKS (Kubernetes)
- 9 Model Monitoring and Surveillance
- 10 CI/CD for ML Deployment
- 11 Complete Implementation with the SDK
- 12 Patterns and Best Practices
- 13 Summary and Key Points
- 14 Glossary
- 15 Model Catalog and Fine-tuning
- 16 Online Endpoints – Real-time Inference
- 17 CLI Commands for Deployment
- 18 Deployment on AKS
- 19 Key Points Summary
- 20 Review Questions
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