AWS offers over 30 AI/ML services spanning computer vision, NLP, speech, forecasting, recommendations, and generative AI. For the AWS AI Practitioner exam (AIF-C01), you need to know which service solves which business problem — not the underlying implementation details.

AWS AI services are categorised into: AI Services (pre-built, no ML expertise needed), ML Services (SageMaker for custom model development), and Generative AI Services (Bedrock).

Key Points

  • AI Services: fully managed, pre-built models via API — no ML training required
  • ML Services: Amazon SageMaker — end-to-end platform for building custom ML models
  • Generative AI: Amazon Bedrock — managed access to foundation models
  • Vision: Rekognition (image/video analysis), Textract (document extraction)
  • Language: Comprehend (NLP), Translate (translation), Kendra (intelligent search)
  • Speech: Transcribe (speech-to-text), Polly (text-to-speech)
  • Conversational: Lex (chatbots), Q (generative AI assistant for business)
  • Forecasting & Recommendations: Forecast (time series), Personalize (recommendations)
  • Code: Amazon Q Developer (formerly CodeWhisperer) — AI coding assistant
  • Fraud & Health: Fraud Detector, HealthLake, Comprehend Medical
CategoryServiceWhat It Does
VisionAmazon RekognitionFace detection, object/scene labels, content moderation, celebrity recognition
DocumentsAmazon TextractExtract text, tables, forms from scanned documents (beyond simple OCR)
NLPAmazon ComprehendSentiment, entities, key phrases, PII detection, topic modelling
SearchAmazon KendraIntelligent enterprise search using NLP across multiple data sources
TranslationAmazon TranslateNeural machine translation for 75+ languages
Speech → TextAmazon TranscribeReal-time and batch audio transcription with speaker diarisation
Text → SpeechAmazon PollyNatural speech synthesis with SSML support, custom lexicons
ChatbotsAmazon LexBuild voice and text chatbots (powers Alexa)
Time SeriesAmazon ForecastAutoML for demand/revenue/capacity forecasting
RecommendationsAmazon PersonalizeReal-time personalisation using the same tech as Amazon.com
FraudAmazon Fraud DetectorReal-time fraud detection without ML expertise

Real-World Example

A retail company can build a complete AI-powered customer service solution without a single ML engineer: use Lex for the chatbot, Comprehend for sentiment analysis, Personalize for recommendations, Rekognition for visual search, and Kendra for product FAQs — all via managed APIs.