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Cloud Fundamentals

AWS AI/ML Services Overview

Map AWS AI/ML services to their specific use cases. Distinguish between building ML models (SageMaker) and using pre-trained APIs.

20 min
Introductory

Learning outcomes

By the end of this lesson, the learner can:

  1. Map AWS AI/ML services to their specific use cases.
  2. Distinguish between "build ML" (SageMaker) vs "use pre-trained APIs".
  3. Identify which service solves a given AI/ML problem.

Two paths: Build vs Use

AWS offers two approaches to AI/ML:

PathServiceUse case
Build your own MLAmazon SageMaker AICustom models, training, deployment
Use pre-trained APIsVarious AI servicesCommon tasks without ML expertise

AI/ML Service-to-Problem Map

Quick Reference

AI/ML Service Quick Reference

SageMaker AI
What it is: Build, train, and deploy ML models
What it's for: Custom ML workflows from data to hosted endpoints
Lex
What it is: Build conversational interfaces
What it's for: Chatbots and voice assistants with NLU
Kendra
What it is: Intelligent enterprise search
What it's for: Natural language search across document corpora
Comprehend
What it is: NLP text analysis
What it's for: Extract entities, sentiment, key phrases from text
Rekognition
What it is: Image and video analysis
What it's for: Labels, faces, moderation, content detection
Textract
What it is: Document text extraction
What it's for: Extract text, forms, tables from PDFs/images
Transcribe
What it is: Speech-to-text
What it's for: Convert audio to text with timestamps
Translate
What it is: Machine translation
What it's for: Real-time or batch language translation
Polly
What it is: Text-to-speech
What it's for: Convert text to lifelike speech
Amazon Q
What it is: Generative AI assistant
What it's for: Contextual answers for developer and business use

Common workflow: Document understanding

Many AI/ML services work together in real-world scenarios:

Document understanding workflow combining multiple AI/ML services

Micro-activity: Match AI/ML Service to Use Case

Micro-Activity

Match AI/ML Service to Use Case

Examples

Choose one, then match it on the right

Characteristics

Select an example first

0 of 5 matched so far.

Knowledge Check

Knowledge Check
1 / 3

Which service would you use to convert recorded customer service calls into searchable text?

Summary

AWS AI/ML services follow a clear split:

  • SageMaker AI — For building custom ML models
  • Pre-trained services — For common AI tasks without ML expertise

The pre-trained services cover vision (Rekognition), language (Comprehend, Translate), speech (Transcribe, Polly), documents (Textract), conversation (Lex), and search (Kendra).

Next lesson

Lesson 2: AWS Analytics Services Overview