ML-Associate.md

AWS Machine Learning – Associate Study Guide

This guide outlines the services, concepts, and workflows you should understand for the ML – Associate exam.

Track detailed topic coverage with: CHECKLIST_ML-Associate.md

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What to Expect on the ML Associate Exam

• Covers the full ML lifecycle: data prep, training, deployment, and evaluation
• Emphasis on SageMaker built-in capabilities
• Conceptual understanding of ML models and metrics
• Moderate depth on security, cost, and automation

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Key Learning Areas

Data Prep & Ingestion

• S3, Athena, Glue, and Redshift Spectrum
• Data profiling and wrangling using Glue DataBrew and SageMaker Data Wrangler

Model Training & Selection

• SageMaker Studio and Notebooks
• Built-in algorithms and hyperparameter tuning
• Choosing the right model type

Model Deployment

• Real-time inference with endpoints
• Batch Transform
• Model Registry and basic MLOps

Evaluation & Metrics

• Confusion matrix
• Accuracy, precision, recall, F1
• MAE, RMSE, R² for regression

Security & Cost

• KMS encryption for data and models
• IAM roles and scoped access
• Spot training and billing optimization

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Additional Resources

• ML Associate Exam Guide (PDF)
• SageMaker Docs
• AWS Machine Learning Blog

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Next Steps

• Use this guide to shape your study
• Track detailed topic progress in CHECKLIST_ML-Associate.md
• Write and review service .md entries in your knowledge base