AWS Machine Learning Engineer Associate (MLA-C01) — Reference Notes

A concise, print-friendly reference guide for the AWS Certified Machine Learning Engineer – Associate (MLA-C01) learning path.

The notes focus on the concepts and service distinctions that are easiest to confuse across data engineering, modeling, deployment, MLOps, monitoring, security and generative AI.

Inside you’ll find:

• 39 focused topic sections
• Data engineering with S3, Glue, Athena, streaming and Feature Store
• Feature engineering, leakage and missing data
• Classification, regression and clustering metrics
• SageMaker built-in algorithms
• Hyperparameter tuning and distributed training
• Script Mode vs custom containers
• Training cost and performance patterns
• Real-time, serverless, asynchronous and batch inference
• Multi-model endpoints, shadow and canary deployments
• Inference Recommender
• SageMaker Pipelines and Model Registry
• Monitoring, drift, bias and explainability
• IAM, KMS, Macie and private connectivity
• Bedrock, RAG, fine-tuning, guardrails, evaluations and agents
• Quick decision maps and memory hooks
• Print-friendly layout with space for notes

Designed as a compact reference for reviewing AWS ML and MLOps concepts without turning them into a long textbook.

Independent educational resource. Not affiliated with or endorsed by Amazon Web Services, Inc.