3.1.4. AWS AI Services for Common Business Needs
💡 First Principle: AWS AI services are pre-trained, fully managed models that require zero ML expertise. They exist because many business problems—text extraction, translation, sentiment analysis, image recognition—have been solved well enough that training custom models is unnecessary. The exam tests whether you recognize when an AI service is sufficient and when custom training is needed.
| AI Service | What It Does | No Custom Model Needed For |
|---|---|---|
| Amazon Rekognition | Image/video analysis | Face detection, object labeling, content moderation, celebrity recognition |
| Amazon Textract | Document text extraction | OCR, form data extraction, table extraction from scanned documents |
| Amazon Comprehend | NLP analysis | Sentiment, entities, key phrases, language detection, PII detection |
| Amazon Comprehend Medical | Medical NLP | Medical entity extraction, relationship detection, ICD-10 codes |
| Amazon Translate | Language translation | Real-time and batch text translation across 75+ languages |
| Amazon Transcribe | Speech-to-text | Audio/video transcription, speaker identification, custom vocabularies |
| Amazon Polly | Text-to-speech | Natural-sounding speech generation from text |
| Amazon Lex | Conversational AI | Chatbots, voice assistants, IVR systems |
| Amazon Personalize | Recommendations | Product recommendations, personalized search, content ranking |
| Amazon Fraud Detector | Fraud detection | Online payment fraud, account takeover, fake account detection |
| Amazon Kendra | Intelligent search | Enterprise document search with natural language queries |
| Amazon Forecast | Time-series forecasting | Demand forecasting, inventory planning, capacity planning |
⚠️ Exam Trap: If a question describes a standard NLP task (sentiment analysis, entity extraction) and the answer choices include both Amazon Comprehend and a custom SageMaker model, Comprehend is almost always correct—unless the question specifically mentions "domain-specific entities" or "custom categories" that Comprehend doesn't support. The exam penalizes over-engineering.
Reflection Question: A logistics company needs to: (1) extract delivery addresses from scanned invoices, (2) translate customer emails from Spanish to English, and (3) predict package delivery times based on historical route data. Which of these needs a custom model and which can use an AI service?
