AWS Machine Learning: Picking the Right Service for the SAA Exam
In the SAA exam, a machine learning question usually isn’t testing whether you can train a model — it’s testing whether you can recognize the input data type and the task the prompt describes. The question drops a scenario — “automatically generate subtitles for videos”, “analyze sentiment in customer comments”, “extract data from scanned invoices” — and almost always there’s exactly one service that’s the intended answer.
The key insight: nearly every service in this section is a pre-trained, fully managed AI service — you just call an API, hand it data, and get a result; you don’t build a model. The one exception is SageMaker, where you build/train/deploy your own model. You get roughly 90 seconds per question, so recognition has to be nearly reflexive. This article is that map: for each service I summarize what it does, the core features, the use case, and most importantly — the keywords that give it away in the prompt.
Important note: These are only basic cues for quickly picking an answer under exam pressure. In the real world, choosing an AI/ML service demands far more thought: accuracy on real data, cost per request, data residency and compliance requirements, whether you need to train on your own data, and whether a pre-trained service is good enough or you must build with SageMaker. A keyword rarely maps to exactly one production-correct choice the way it does on the exam.
1. Amazon Rekognition — Image and Video Analysis
Rekognition is a pre-trained computer vision service (computer vision — letting machines “see” and understand the contents of images/video) that analyzes images and video through an API.
- Detects objects and scenes (labels for things and settings in an image).
- Facial analysis (age, emotion, wearing glasses…), plus face comparison and recognition.
- Celebrity recognition.
- Content moderation — flags inappropriate/unsafe content (nudity, violence).
- Reads text that appears in images (text in image), detects personal protective equipment (PPE).
- Integrates with Kinesis Video Streams for real-time video analysis.
Use case: Analyze photo/video libraries, moderate user-uploaded content, face-based verification, count people in a frame.
Keywords: image / video analysis, face detection/recognition, object/scene detection, content moderation, inappropriate / unsafe content.
2. Amazon Transcribe — Speech to Text
Transcribe uses ASR to convert spoken audio into text.
- Automatic speech recognition with high accuracy.
- Automatically redacts sensitive PII from the transcript.
- Automatic language identification, multi-language support.
- Custom vocabulary (a dictionary for domain-specific terms) and speaker diarization (who said what).
Use case: Transcribe calls/meetings, generate subtitles/captions for video, turn voicemail into text.
Keywords: speech to text, transcribe audio, subtitles / captions, convert audio to text.
3. Amazon Polly — Text to Speech
Polly does the reverse of Transcribe: it uses TTS to turn text into lifelike speech.
- Many natural-sounding voices (neural voices) across languages.
- Control delivery with SSML (pauses, emphasis, speed).
- Lexicons (custom pronunciation dictionaries) and Speech Marks (sync text with audio for lip-sync/highlight effects).
Use case: Give apps a voice, turn articles into audio, support the visually impaired, voice a virtual assistant.
Keywords: text to speech, generate voice / audio from text, lifelike speech, read text aloud.
4. Amazon Translate — Language Translation
Translate is neural machine translation (translating in the context of the whole sentence rather than word by word), converting text between languages naturally.
- Translates across many language pairs while preserving context.
- Localizes app/website content.
- Batch or real-time translation, with custom terminology support.
Use case: Localize multi-language UI/content, translate user-generated text, translate documents in bulk.
Keywords: translate, language translation, localization, multilingual.
5. Amazon Lex + Amazon Connect — Chatbots and Cloud Contact Center
Lex is the service for building conversational chatbots, using the same ASR + NLU technology as Alexa. Connect is a cloud-based customer contact center that can use Lex to power voice/chat bots.
- Lex: understands users via intents (what they want) and slots (the information to collect), calling Lambda for business logic.
- Connect: a virtual contact center with no upfront infrastructure, integrating Lex to automate calls.
- Lex + Connect together build an intelligent IVR.
Use case: Customer-support chatbots, conversational booking/ordering assistants, automated phone lines.
Keywords: chatbot, conversational interface, contact center / call center, IVR.
6. Amazon Comprehend — Text Analysis (NLP)
Comprehend is a pre-trained NLP service that reads and extracts information from free-form text.
- Sentiment analysis (positive/negative/neutral).
- Entity and key-phrase extraction.
- Language detection, PII detection.
- Topic modeling — automatically groups documents by topic.
Use case: Analyze customer feedback, auto-classify/label documents by topic, measure sentiment on social media.
Keywords: sentiment analysis, NLP, extract entities / key phrases, find topics in text.
7. Amazon Comprehend Medical — NLP for Medical Text
Comprehend Medical is a version of Comprehend specialized for clinical text, understanding medical terminology that plain Comprehend often handles poorly.
- Detects PHI in records.
- Extracts medical entities: conditions, medications, dosages, symptoms.
- Links to standard medical coding systems (ICD-10-CM for diagnoses, RxNorm for medications).
Use case: Extract structured information from doctor’s notes, patient records, and prescriptions.
Keywords: medical / clinical text, PHI, healthcare NLP, doctor's notes / patient records.
8. Amazon SageMaker AI — Platform to Build Your Own ML Models
SageMaker is a fully managed platform for you to build, train, and deploy your own machine learning models — fundamentally different from the pre-trained services above.
- Notebooks for data exploration, plus a library of built-in algorithms.
- Training jobs and hyperparameter tuning.
- Deploy a model as an endpoint for real-time inference, or batch transform for bulk processing.
- Ground Truth for data labeling.
Use case: A data science team building a custom model for a problem no pre-trained service can solve — this is the only “build it yourself” service in the group.
Keywords: build / train / deploy your own model, custom ML model, data scientists, machine learning platform.
9. Amazon Kendra — Intelligent Document Search
Kendra is an ML-powered enterprise search service that lets you search an internal document store with natural-language questions instead of just keyword matching.
- Understands natural-language questions and returns a direct answer extracted from the documents, not just a list of links.
- Built-in connectors for many sources (S3, SharePoint, Salesforce…).
- Incremental learning from user feedback.
Use case: Internal Q&A systems, searching a company’s document store / knowledge base.
Keywords: document search, enterprise search, knowledge base, natural language search.
10. Amazon Personalize — Real-Time Personalized Recommendations
Personalize is a recommendation engine using the same technology as Amazon.com, producing personalized recommendations in real time.
- Recommends products/content tailored to each user.
- Personalized ranking (re-orders a list by preference) and “similar items” suggestions.
- Updates in real time based on new user behavior.
Use case: Product recommendations in e-commerce, content suggestions, personalized email marketing.
Keywords: recommendations, personalization, "recommended for you", personalized ranking.
11. Amazon Textract — Extract Data from Documents
Textract uses advanced OCR to extract not just text but structured data (key-value pairs in forms, tables, handwriting) from scanned documents, PDFs, and images.
- Reads text, forms (key-value), and tables from documents.
- Recognizes handwriting too.
- Preserves the data structure so it can flow straight into a processing system.
Use case: Process invoices, IDs, forms, and financial/tax/medical documents.
Keywords: extract text from documents, scanned documents / PDF, forms & tables, OCR, process invoices / IDs.
Tips & Tricks — Recognize the Keyword and Pick the Service
This is the most important part for the exam. Read the prompt, catch the keyword, map it straight to a service.
By input data type
| When the prompt says… | Pick |
|---|---|
| Images, video, faces, objects | Rekognition |
| Spoken audio → need text out | Transcribe |
| Text → need speech out | Polly |
| Text that needs translating to another language | Translate |
| Text to analyze for sentiment / entities / topics | Comprehend |
| Medical / clinical text | Comprehend Medical |
| Scanned documents / PDF / forms / tables | Textract |
| Conversation / chatbot / contact center | Lex (+ Connect) |
| A search query over a document store | Kendra |
| Behavior history → need recommendations | Personalize |
Pre-trained or build your own
| When the prompt says… | Pick |
|---|---|
| ”data scientists”, “train your own model”, “custom model”, “custom algorithm” | SageMaker |
| A common AI task (image, speech, translation, NLP…) via a ready API | The matching pre-trained service |
Wrapping Up
One line to remember:
Recognize the data type and the task first, and the service falls out.
In the exam, the keyword → service reflex saves you precious time. But in real life, the right question isn’t “which service matches the keyword”, but whether a pre-trained service is accurate enough for the problem, what it costs per request, what compliance constraints the data carries, and when it’s worth training your own model with SageMaker.