Book Content
chapters • 10h52m total length
1. Machine Learning and Its Life Cycle in the Cloud
2. Introducing Amazon SageMaker Studio
3. Data Preparation with SageMaker Data Wrangler
4. Building a Feature Repository with SageMaker Feature Store
5. Building and Training ML Models with SageMaker Studio IDE
6. Detecting ML Bias and Explaining Models with SageMaker Clarify
7. Hosting ML Models in the Cloud: Best Practices
8. Jumpstarting ML with SageMaker JumpStart and Autopilot
9. Training ML Models at Scale in SageMaker Studio
10. Monitoring ML Models in Production with SageMaker Model Monitor
11. Operationalize ML Projects with SageMaker Projects, Pipelines and Model Registry
