Amazon SageMaker Best Practices

Table of Contents

1. Amazon SageMaker Overview
2. Data Science Environments
3. Data Labeling with Amazon SageMaker Ground Truth
4. Data Preparation at Scale Using Amazon SageMaker Data Wrangler and Processing
5. Centralized Feature Repository with Amazon SageMaker Feature Store
6. Training and Tuning at Scale
7. Profile Training Jobs with Amazon SageMaker Debugger
8. Managing Models at Scale Using a Model Registry
9. Updating Production Models Using Amazon SageMaker Endpoint Production Variants
10. Optimizing Model Hosting and Inference Costs
11. Monitoring Production Models with Amazon SageMaker Model Monitor and Clarify
12. Machine Learning Automated Workflows
13. Well-Architected Machine Learning with Amazon SageMaker
14. Managing SageMaker Features Across Accounts