ML Algorithms
Local Training and Testing
Remote Training
Distributed Training
Monitoring Training Jobs
Debugging Training Jobs
Hyperparameter Optimization
Summary
Exam Essentials
Review Questions
Chapter 9: Model Evaluation
Experiment Management
Metrics and Visualization
Summary
Exam Essentials
Review Questions
Chapter 10: Model Deployment and Inference
Deployment for AI Services
Deployment for Amazon SageMaker
Advanced Deployment Topics
Summary
Exam Essentials
Review Questions
Chapter 11: Application Integration
Integration with On-Premises Systems
Integration with Cloud Systems
Integration with Front-End Systems
Summary
Exam Essentials
Review Questions
13 PART III: Machine Learning Well-Architected Lens Chapter 12: Operational Excellence Pillar for ML Operational Excellence on AWS Summary Exam Essentials Review Questions Chapter 13: Security Pillar Security and AWS Secure SageMaker Environments AI Services Security Summary Exam Essentials Review Questions Chapter 14: Reliability Pillar Reliability on AWS Change Management for ML Failure Management for ML Summary Exam Essentials Review Questions Chapter 15: Performance Efficiency Pillar for ML Performance Efficiency for ML on AWS Summary Exam Essentials Review Questions Chapter 16: Cost Optimization Pillar for ML Common Design Principles Cost Optimization for ML Workloads Summary Exam Essentials Review Questions Chapter 17: Recent Updates in the AWS AI/ML Stack New Services and Features Related to AI Services New Features Related to Amazon SageMaker Summary Exam Essentials
14 Appendix Answers to the Review Questions Chapter 1: AWS AI ML Stack Chapter 2: Supporting Services from the AWS Stack Chapter 3: Business Understanding Chapter 4: Framing a Machine Learning Problem Chapter 5: Data Collection Chapter 6: Data Preparation Chapter 7: Feature Engineering Chapter 8: Model Training Chapter 9: Model Evaluation Chapter 10: Model Deployment and Inference Chapter 11: Application Integration Chapter 12: Operational Excellence Pillar for ML Chapter 13: Security Pillar
