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AWS's AI: foundation models as a service with Bedrock and the end-to-end ML platform with SageMaker — intelligence without managing GPUs.

FOCUS · GOVERNED AIModels, guardrails and company data: generative AI inside the enterprise perimeter
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01 · What it is

Amazon Bedrock & SageMaker, made clear.

Bedrock exposes the best foundation models (Anthropic Claude, Meta Llama, Amazon Nova and others) via API, with guardrails, knowledge bases for RAG and agents. SageMaker covers the full ML cycle: from notebooks to the model in production with MLOps.

1 API
many models: you change engines without redoing the application
Managed RAG
Knowledge Bases: company documents in the prompt, with citations
MLOps
SageMaker: training, registry and deploy with governance
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02 · How to use it well

The things that make the difference.

The AI stack

Applications & copilotswhere AI meets the user
Bedrock · GenAI
Agents & Guardrails
SageMaker · ML
generative · control · predictive
Knowledge Bases (RAG)company context, governed
Data on S3the fuel: yours, not someone else's
From data to copilot, with the right brakes

Model choice

Claude for reasoning, light models for volume: the right engine per use case and budget.

Guardrails

Filters on input and output, forbidden topics, masked PII: AI that respects company policies.

Agents

Actions beyond text: AI that queries systems and completes flows, under supervision.

Fine-tuning & distillation

The model adapted to the domain when needed: more quality, fewer tokens.

03 · In depth

Models, managed RAG and guardrails

Bedrock exposes via API the models from Anthropic (Claude), Amazon (Nova), Meta, Mistral and others: chosen by task and cost, versioned, evaluated. Knowledge Bases manages RAG end-to-end (ingestion, embeddings, vector store, citations); Agents orchestrates company tools and APIs; Guardrails filters input/output by policy; data does NOT train the models and stays within the AWS perimeter (PrivateLink included).

  • Multi-modello — Claude, Nova, Llama, Mistral behind one API: the right model per task
  • Knowledge Bases — managed RAG with citations: from S3 documents to grounded answers
  • Agents — orchestrated function calling: AI executing on YOUR APIs
  • Guardrails — configurable content and PII filters: policy applied at runtime
  • PrivateLink — inference inside the VPC: prompts never on the internet
  • Provisioned throughput — reserved capacity for stable production workloads
04 · Numbers and lifecycle

The numbers that matter.

0
use of your data for training: contractual
6+
the model families in the catalog
Token
the cost meter: budgets and alerts like any other service
VPC
inference via PrivateLink: closed network perimeter
GenAI in production is governance plus evaluation: we choose the models on YOUR cases, measure quality and tokens, and the guardrails stay on.
05 · Use cases

Where it really pays off.

Assistants on company knowledge

Manuals, contracts and procedures queryable in natural language: RAG done right.

Document automation

Orders, delivery notes and invoices read and structured: AI that removes data entry.

Forecasts & anomalies

SageMaker on factory and sales data: prediction at the service of operations.

Useful AI starts from tidy data and ends in production: with governance, measured costs and real use cases — the rest is a demo.