AWS AI & ML Services Cheat Sheet

• AWS provides a comprehensive suite of AI and Machine Learning services spanning generative AI, ML platforms, AI services, and responsible AI.
• Services range from pre-trained APIs requiring no ML expertise to fully managed platforms for custom model training and deployment.
• This cheat sheet covers services relevant to the AWS AI Practitioner (AIF-C01), ML Engineer Associate (MLA-C01), and Solutions Architect certifications.

Generative AI Services

Amazon Bedrock

• Fully managed service to build generative AI applications using foundation models (FMs).
• Access models from AI21 Labs, Anthropic (Claude), Cohere, Meta (Llama), Mistral, Stability AI, and Amazon (Titan).
• No infrastructure to manage – serverless API access to foundation models.
• Knowledge Bases – implement RAG (Retrieval Augmented Generation) by connecting FMs to your data sources (S3, databases).
• Agents – create AI agents that can plan, execute multi-step tasks, and call APIs/Lambda functions.
• Guardrails – control model outputs with content filters, denied topics, PII redaction, and word filters.
• Model Evaluation – evaluate and compare FM performance on your specific tasks.
• Fine-tuning – customize models with your data (continued pre-training or instruction fine-tuning).
• Provisioned Throughput – reserve model capacity for consistent performance.
• Data is not used to train base models – data privacy by default.

Amazon Q

• Amazon Q Business – AI assistant for enterprise that connects to company data (S3, SharePoint, Confluence, Salesforce, etc.).
• Amazon Q Developer – AI coding assistant for IDEs with code generation, debugging, transformation, and security scanning.
• Amazon Q in QuickSight – natural language queries for BI dashboards.
• Amazon Q in Connect – AI-powered agent assistance for contact centers.
• Respects existing access controls and permissions – users only see answers from data they can access.

Amazon Titan Models

• Titan Text – text generation, summarization, classification, Q&A.
• Titan Embeddings – convert text to numerical vectors for search, RAG, and recommendations.
• Titan Image Generator – generate and edit images from text prompts.
• Titan Multimodal Embeddings – embeddings for both text and images.
• All Titan models include built-in watermarking for generated content.

ML Platform

Amazon SageMaker

• Fully managed ML platform for building, training, and deploying models at scale.
• SageMaker Studio – integrated IDE for ML development (notebooks, experiments, pipelines).
• Built-in algorithms – XGBoost, Linear Learner, K-Means, Image Classification, Object Detection, etc.
• Training – managed training infrastructure with spot instances (up to 90% savings).
• SageMaker Pipelines – CI/CD for ML (MLOps) with automated workflow orchestration.
• Model Registry – catalog, version, and manage trained models.
• SageMaker ML Lineage Tracking – automatically tracks end-to-end relationships from data → training → model → endpoint; supports cross-account lineage sharing via RAM for enterprise governance and compliance audits.
• Endpoints – real-time inference, batch transform, async inference, serverless inference.
• SageMaker Canvas – no-code ML for business analysts (visual interface).
• SageMaker JumpStart – pre-trained foundation models and ML solutions ready to deploy.
• SageMaker Clarify – detect bias in data/models and explain model predictions (SHAP values).
• SageMaker Data Wrangler – visual data preparation and feature engineering.
• SageMaker Feature Store – centralized repository for ML features (online + offline store).
• SageMaker Ground Truth – data labeling with human annotators and active learning.
• SageMaker Model Monitor – detect data drift, model quality drift, and bias drift in production.

AI Services (Pre-trained APIs)

Natural Language Processing (NLP)

• Amazon Comprehend – NLP service for sentiment analysis, entity recognition, key phrases, language detection, PII detection, topic modeling.
• Amazon Comprehend Medical – extract medical entities (conditions, medications, dosages) from clinical text.
• Amazon Translate – neural machine translation for 75+ languages with custom terminology support.
• Amazon Transcribe – speech-to-text (ASR) with speaker identification, custom vocabulary, PII redaction.
• Amazon Transcribe Medical – medical speech-to-text for clinical documentation.

Vision

• Amazon Rekognition – image and video analysis (object/scene detection, face analysis, text in images, content moderation, celebrity recognition, custom labels).
• Amazon Textract – extract text, tables, and forms from documents (beyond basic OCR). Supports invoices, receipts, ID documents.

Speech

• Amazon Polly – text-to-speech with neural and standard voices, SSML support, speech marks for lip-sync.
• Amazon Lex – build conversational chatbots with automatic speech recognition (ASR) and natural language understanding (NLU). Powers Alexa technology.

Search & Recommendations

• Amazon Kendra – intelligent enterprise search powered by ML with natural language queries and document ranking.
• Amazon Personalize – real-time personalized recommendations (similar to Amazon.com) without ML expertise.

Forecasting & Other

• Amazon Forecast – time-series forecasting using ML (demand planning, resource planning).
• Amazon Fraud Detector – identify potentially fraudulent online activities using ML.
• Amazon CodeWhisperer (now Amazon Q Developer) – AI-powered code suggestions in IDEs.

Data & Analytics for ML

• AWS Glue – serverless ETL with built-in ML transforms (FindMatches for deduplication).
• Amazon Athena ML – run ML inference from SQL queries using SageMaker models.
• Amazon Redshift ML – create, train, and deploy ML models using SQL (uses SageMaker Autopilot).
• Amazon Kinesis – real-time data streaming for ML inference on streaming data.
• AWS Lake Formation – build secure data lakes as training data sources.

Responsible AI

• Amazon Bedrock Guardrails – content filters, denied topics, PII redaction, hallucination reduction (grounding checks).
• SageMaker Clarify – pre-training bias detection (CI, DPL, KL metrics) and post-training bias detection (DPPL, DI, AD).
• SageMaker Model Monitor – continuous monitoring for data quality, model quality, bias drift, and feature attribution drift.
• Model Explainability – SHAP values for feature importance and individual prediction explanations.
• Amazon Titan watermarking – invisible watermarks in generated images for content authenticity.
• AWS AI Service Cards – transparency documentation for AWS AI services.
• Human-in-the-loop – Amazon Augmented AI (A2I) for human review of ML predictions.

Infrastructure for AI/ML

• AWS Trainium – custom chip optimized for deep learning training (used in EC2 Trn1 instances).
• AWS Inferentia – custom chip optimized for inference (used in EC2 Inf2 instances). Up to 40% better price-performance than GPU.
• Amazon EC2 P5/P4d instances – NVIDIA GPU instances for training and inference.
• Amazon EC2 G5/G6 instances – GPU instances for graphics and ML inference.
• AWS Neuron SDK – compile and optimize models for Trainium and Inferentia chips.
• Amazon S3 – primary storage for training data, model artifacts, and outputs.
• FSx for Lustre – high-throughput file system for training data (integrates with S3).

Key Concepts for Certification

ML Workflow

• Data Collection → Data Preparation (cleaning, feature engineering) → Model Training → Evaluation → Deployment → Monitoring

Model Types

• Supervised Learning – labeled data (classification, regression). Examples: fraud detection, price prediction.
• Unsupervised Learning – no labels (clustering, anomaly detection). Examples: customer segmentation, topic modeling.
• Reinforcement Learning – agent learns through rewards (robotics, game playing, recommendations).
• Foundation Models – large pre-trained models fine-tuned or used via prompting (GPT, Claude, Llama, Titan).

RAG (Retrieval Augmented Generation)

• Combines a foundation model with external knowledge retrieval to provide accurate, up-to-date, and cited answers.
• AWS implementation: Bedrock Knowledge Bases + vector database (OpenSearch Serverless, Aurora PostgreSQL, Pinecone).
• Process: Query → Retrieve relevant chunks from knowledge base → Augment prompt with context → Generate answer.

Prompt Engineering

• Zero-shot – ask directly without examples.
• Few-shot – provide examples in the prompt.
• Chain-of-thought – instruct the model to reason step by step.
• System prompts – set behavior, persona, and constraints.

AWS Certification Exam Practice Questions

1. A company wants to build a chatbot that answers questions using their internal documentation stored in S3 and Confluence. The answers must cite sources. Which AWS service and feature combination is most appropriate?
   1. Amazon Lex with Lambda
   2. Amazon Bedrock with Knowledge Bases (RAG)
   3. Amazon Kendra with Lex
   4. Amazon Comprehend with Q Business
2. A team needs to detect if their ML model exhibits bias against a protected demographic group before deploying to production. Which service should they use?
   1. Amazon Bedrock Guardrails
   2. Amazon Rekognition
   3. SageMaker Clarify
   4. Amazon Comprehend
3. An application needs to extract structured data (tables, key-value pairs) from scanned invoices and receipts. Which service is purpose-built for this?
   1. Amazon Rekognition
   2. Amazon Comprehend
   3. Amazon Textract
   4. Amazon Bedrock
4. A generative AI application must prevent the model from discussing competitor products and must redact any PII in responses. Which feature provides these controls?
   1. SageMaker Model Monitor
   2. Amazon Bedrock Guardrails
   3. Amazon Comprehend PII detection
   4. AWS WAF
5. A company needs the lowest cost per inference for deploying a trained deep learning model at high throughput. Which AWS hardware is optimized for this?
   1. EC2 P5 instances (NVIDIA GPU)
   2. EC2 G5 instances
   3. EC2 Inf2 instances (AWS Inferentia2)
   4. EC2 Trn1 instances (AWS Trainium)

Related Posts

• AWS Certified AI Practitioner (AIF-C01) Exam Learning Path
• AWS Certified ML Engineer – Associate Exam Learning Path
• AWS Certified Solutions Architect – Associate Exam Learning Path
• AWS Certification Exam Cheat Sheet
• Amazon Bedrock – Generative AI Service
• Amazon Q Business & Q Developer

References

Amazon Bedrock User Guide

Amazon SageMaker Developer Guide

Amazon Q Business User Guide

AWS AI/ML Services Overview