metodologia-aws-architecture-implementation

What problem does it solve?

Deploying scalable GenAI architectures on AWS is complex due to fragmented services, security requirements, and deployment pipelines. This skill provides a cohesive, end-to-end guide to implement Bedrock, SageMaker, OpenSearch vector stores, API Gateway, and cost/control mechanisms in a reproducible way.

Core Features & Use Cases

• Bedrock knowledge base setup, agent design, and guardrails for compliant GenAI workflows.
• SageMaker pipelines for training, registry, endpoints, and monitoring with automated CI/CD.
• OpenSearch vector stores for RAG-enabled retrieval and semantic search.
• Security hardening, IAM least-privilege, VPC endpoints, KMS, and audit trails.
• Cost controls and observability through CloudWatch dashboards, budgets, and runbooks.
• Use Case: Build a regulated enterprise GenAI solution with multi-account isolation and auditable deployment gates.

Quick Start

Follow this guide to start implementing an AWS GenAI architecture using Bedrock and SageMaker today.