Question 1

What is the difference between Amazon SageMaker and Amazon Bedrock?

Accepted Answer

Amazon SageMaker is a comprehensive managed ML platform for building, training, and deploying custom ML models. It provides managed Jupyter notebooks, distributed training infrastructure, automatic hyperparameter tuning, model registries, MLOps pipelines, feature stores, and real-time and batch inference endpoints. SageMaker is the right choice when you need to train custom models on your own data. Amazon Bedrock is a fully managed service providing API access to foundation models (FMs) from Anthropic, AI21 Labs, Cohere, Meta, Mistral, Stability AI, and Amazon's own Titan models. Bedrock is the right choice when you want to use pre-trained foundation models without managing training infrastructure — for LLM-powered applications, RAG pipelines, and AI agents.

Question 2

What AWS AI services are available without any ML expertise?

Accepted Answer

AWS provides a category of pre-built AI APIs requiring zero ML expertise: Amazon Rekognition (image and video analysis — object detection, face recognition, content moderation, OCR); Amazon Comprehend (NLP — sentiment analysis, entity extraction, language detection, topic modelling); Amazon Textract (document extraction — structured data extraction from PDFs and images including tables and forms); Amazon Transcribe (speech-to-text with speaker diarisation); Amazon Polly (text-to-speech with 60+ voices in 29 languages); Amazon Translate (neural machine translation across 75+ languages); Amazon Forecast (time series forecasting without ML expertise); and Amazon Personalize (personalisation and recommendation engine). These services are consumed via REST API calls and billed per unit of input processed.

Question 3

What is Amazon Bedrock Knowledge Bases and how does it enable RAG?

Accepted Answer

Amazon Bedrock Knowledge Bases provides fully managed RAG (Retrieval-Augmented Generation) infrastructure. You connect data sources (S3, Confluence, SharePoint, Salesforce, web crawler) and Bedrock automatically chunks documents, generates embeddings using an Amazon Titan embedding model, stores them in a managed vector database (OpenSearch Serverless by default, or Aurora PostgreSQL pgvector, Pinecone, Redis Enterprise, MongoDB Atlas), and handles semantic retrieval at query time. This eliminates the need to build and manage your own chunking pipeline, embedding generation, vector database, and retrieval logic — reducing RAG implementation from weeks to hours.

Question 4

How does Amazon SageMaker Feature Store work?

Accepted Answer

SageMaker Feature Store is a centralised repository for ML features that serves both online and offline use cases. The Online Store (backed by Amazon DynamoDB) provides single-digit millisecond latency feature retrieval for real-time inference — serving ML models in production with fresh feature values. The Offline Store (backed by Amazon S3 with Glue Data Catalog integration) provides a historical record of all feature values for model training and backtesting. Feature groups define the schema; features are ingested once and served from both stores simultaneously. This eliminates the training-serving skew problem: training data and inference data use identical feature computation logic.

AWS AI & Machine Learning Services — Complete Enterprise Reference

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Amazon Bedrock — Foundation Model Access and Agent Infrastructure

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