The consolidated AWS ML Associate CheatSheet — 50 concepts you need to know

2026-05-20 · Source: Machine Learning on Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cloud Computing & IT Infrastructure, Data Science & Analytics · Depth: Intermediate, long

Summary

This consolidated cheat sheet outlines 50 essential concepts for the AWS Machine Learning Associate exam, covering a broad spectrum of AWS services and machine learning fundamentals. It details various AWS instance types like On-demand, Spot, and Reserve, along with data formats such as CSV and Protobuf RecordIO, and ingestion modes like Pipe and File for SageMaker. The guide explains core ML algorithms including Linear Regression, XGBoost, K-Means, and PCA, alongside crucial metrics like Accuracy, Precision, and Recall. It also addresses practical challenges such as imbalanced datasets, overfitting, and underfitting, and describes regularization techniques like L1/L2 penalization and Dropout. Furthermore, the cheat sheet covers AWS services for data cleaning (Data Wrangler, Glue DataBrew), ML tasks (Comprehend, Recognition, Transcribe, Polly, Translate, Lex), storage (EC2, EBS, EFS, RDS, Aurora, DynamoDB, Redshift), and advanced SageMaker features like Multi-Model Endpoints, Clarify, Model Monitor, Hyperband, Autopilot, Debugger, and Experiments. Security aspects like Security Groups, NACLs, IGW, and NAT Gateway are also included, along with deployment strategies such as Blue-Green, Canary, Rolling, A/B Testing, and Shadow Deployment.

Key takeaway

For Machine Learning Engineers preparing for the AWS ML Associate exam, focus on understanding the practical applications and distinctions between AWS services and ML concepts. Prioritize mastering instance types for cost-performance optimization, data handling modes for efficiency, and the specific use cases for SageMaker tools like Clarify, Model Monitor, and Autopilot. Your ability to differentiate between deployment strategies and security mechanisms like Security Groups versus NACLs will be critical for success.

Key insights

The AWS ML Associate exam emphasizes practical knowledge across core ML concepts and AWS service implementations.

Principles

• Optimize compute costs with appropriate AWS instance types.
• Match data formats and ingestion modes to workload needs.
• Select algorithms and metrics based on problem type.

Method

For SageMaker hyperparameter tuning, Hyperband prioritizes speed by aggressively eliminating poor configurations, while Bayesian Optimization focuses on accuracy through intelligent parameter selection.

In practice

• Use SMOTE for imbalanced datasets, augmentation for images.
• Apply L1 for feature selection, L2 for coefficient shrinkage.
• Employ SageMaker Profiler for training code bottlenecks.

Topics

• AWS SageMaker
• Machine Learning Algorithms
• Model Deployment Strategies
• Data Management & ETL
• MLOps & Model Monitoring

Best for: AI Student, Machine Learning Engineer, Data Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Machine Learning on Medium.