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AWS ML Engineer Associate study guide

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Lessons

  1. Data Cataloging, Governance, and Querying
  2. Ingesting Batch and Streaming Data for ML
  3. High-Performance Storage for Training and In
  4. Data Validation, Schema Drift, and Quality R
  5. Data Labeling with SageMaker Ground Truth &
  6. Handling Missing Values, Outliers & Class Im
  7. Endpoint Auto-Scaling & Hardware Acceleratio
  8. Production Deployment Patterns & Traffic Shi
  9. ML Evaluation Metrics & Validation Strategie
  10. Experiment Tracking with SageMaker Experimen
  11. Model Governance with SageMaker Model Regist
  12. Distributed Feature Engineering with AWS Glu
  13. Interactive Data Preparation with SageMaker
  14. Feature Management with SageMaker Feature St
  15. High-Throughput Offline Inference with Batch
  16. Real-Time, Serverless, and Asynchronous Infe
  17. Multi-Model Endpoints & Multi-Container Endp
  18. The Machine Learning Engineering Lifecycle o
  19. AWS MLA-C01 Blueprint, Format & Strategy
  20. ML Cost Optimization & Resource Governance
  21. IAM Roles and Least Privilege for ML — Free Study Guide 2026
  22. Network Isolation, VPC Endpoints & Data Encr
  23. Advanced Pipeline Patterns & Integration
  24. End-to-End MLOps CI/CD & Automated Retrainin
  25. SageMaker Pipelines Core Architecture — Free Study Guide 2026
  26. Foundation Models, JumpStart, and Amazon Bed
  27. SageMaker Built-in Algorithms & Selection Cr
  28. Custom Models with Script Mode and BYOC
  29. Model Bias & Model Explainability Drift
  30. Operational Observability with Amazon CloudW
  31. Data Quality & Model Quality Monitoring
  32. Safety, Guardrails & Generative AI Governanc
  33. Model Documentation & Auditing with Model Ca
  34. Pre-Training and Post-Training Bias with Sag
  35. Hyperparameter Optimization with Automatic M
  36. Distributed Training Strategies & Architectu
  37. SageMaker Training Jobs & Cost Optimization