What you’ll take away.
- Justify a production-oriented GenAI architecture using Amazon Bedrock.
- Integrate RAG, agents and evaluation into a controlled workflow.
- Address identity, monitoring, cost and failure scenarios systematically.
Your training in detail
- Who is it for?
- Experienced developers, cloud engineers and AI engineers with practical AWS, software or data-platform experience.
- Prerequisites
- Solid foundations in cloud architecture, APIs, identity and software delivery. AWS describes the related professional certification as aimed at developers with multiple years of development experience and hands-on generative AI experience.
- Format & planning
- Indicative format: 4 to 5 days with labs and a capstone design. Scope, dates and any exam alignment are agreed in advance.
Agenda: from understanding to application
Our independently developed agenda proposal. Focus areas, lab environment and scope are agreed before booking.
Requirements, risks and quality criteria for a production GenAI use case
Bedrock architecture, model access, identity and secure data flows
RAG, knowledge sources, agents and controlled tool use
Evaluation, guardrails, observability and troubleshooting
Deployment, cost controls and a documented operational handover
Design and review a bounded generative AI application using synthetic data. Document the architecture decision, evaluation cases, security boundaries, operating metrics and fallback route.
Exams, licences & credentials
The course can be aligned with official AWS exam domains on request. The exam, voucher and certification are not automatically included and are confirmed only in writing.
Vendor references & review date
Sources checked on 2026-10-06. These sources describe the technology or external exam requirements. They do not imply a partnership with NextNowa.
Your learning content
These objectives describe the training topic. The specific syllabus, duration, dates, trainer and price are agreed before booking.