1.2.3. Overview of AWS Managed AI/ML Services (Comprehend, Rekognition, etc.)

First Principle: AWS Managed AI Services provide pre-trained intelligence for common tasks, allowing businesses to integrate AI capabilities quickly without needing in-house ML expertise.

For many standard AI tasks, you don't need to build a custom model. You can use an AWS AI Service via a simple API call.

Key Services and Their Use Cases:

• For Vision:
  • Amazon Rekognition: Analyze images and videos to detect objects, faces, text, and inappropriate content.
  • Amazon Textract: Extract text and structured data (from forms and tables) from documents.
• For Language and Speech:
  • Amazon Comprehend: Understand text to find sentiment, entities, and key phrases (NLP).
  • Amazon Translate: Translate text between languages.
  • Amazon Transcribe: Convert speech into text (ASR).
  • Amazon Polly: Convert text into lifelike speech (TTS).
• For Conversational AI:
  • Amazon Lex: Build chatbots and voice assistants.
• For Business Applications:
  • Amazon Forecast: Predict future trends with time-series forecasting.
  • Amazon Personalize: Build recommendation engines.
  • Amazon Fraud Detector: Identify potentially fraudulent online activities.

Scenario: A company wants to add three features to its app: 1) Automatically moderate user-uploaded profile pictures for inappropriate content. 2) Translate user comments into English. 3) Provide an audio read-out of articles.

Reflection Question: Which three AWS AI Services would you map to these three requirements, and why is using these managed services more efficient than building custom models for these tasks?

💡 Tip: When you see a common business problem like "understanding text" or "analyzing images," your first thought should be, "Is there a managed AWS AI Service for this?" before considering a custom build with SageMaker.