Amazon SageMaker has evolved from a pure ML platform into a unified data, analytics, and AI environment — with SageMaker AI for model training, Unified Studio for integrated development, and enterprise governance built in. This practitioner’s guide covers architecture, the Build/Train/Deploy lifecycle, HyperPod, MLOps, cost optimization, security, and the 2024-2026 transformation.
Amazon Bedrock powers generative AI for over 100,000 organizations through nearly 100 foundation models, production-grade AgentCore infrastructure, and enterprise-ready Guardrails. This practitioner’s guide covers architecture, model selection, agents, RAG pipelines, cost optimization, security, and a step-by-step getting started tutorial.
Amazon S3 stores over 500 trillion objects and powers the data layer of modern cloud architecture. This practitioner’s deep dive covers how S3 works under the hood, all 9 storage classes with a decision framework, the six-dimensional pricing model, layered security controls, 2026 updates including S3 Files and S3 Vectors, step-by-step getting started instructions, and production best practices from real-world enterprise deployments.
Amazon Comprehend extracts insights from unstructured text — sentiment, entities, key phrases, PII, and custom classifications — through simple API calls with no ML expertise required. This guide covers all core APIs, custom classification and entity recognition, PII redaction, toxicity detection, pricing, the April 2026 maintenance mode announcement, and a comparison with Azure AI Language.
