A Practical Guide to Generative AI Using Amazon Bedrock — Building, Deploying, and Securing Generative AI Applications

Generative AI with Bedrock — master practical techniques to build, deploy, and secure generative AI applications on Amazon Bedrock while moving models from prototype to production quickly and confidently.

Course Overview

This hands-on course teaches developers, ML engineers, and technical product owners how to design, implement, and operate generative AI systems using Amazon Bedrock and associated tools. You will follow practical examples, build agentic flows, implement retrieval-augmented generation (RAG), and harden deployments with security best practices. Moreover, the lessons focus on real-world patterns so you can apply what you learn immediately in production. :contentReference[oaicite:0]{index=0}

What You’ll Learn

• Foundations of Amazon Bedrock: APIs, models, and AgentCore concepts.
• Designing generative AI agents and multi-step workflows using Bedrock agents.
• Implementing RAG pipelines and connecting knowledge bases for accurate grounding.
• Fine-tuning and customization patterns for foundation models.
• Production deployment strategies, scaling, and observability.
• Security and privacy: credential management, data redaction, and prompt risk mitigation.
• Hands-on examples with Hugging Face tools and text-generation strategies for evaluation and optimization. :contentReference[oaicite:1]{index=1}

Course Content & Modules

1. Introduction to Bedrock — service overview, supported foundation models, and API walkthrough. :contentReference[oaicite:2]{index=2}
2. Agent Design — build and test Bedrock agents and multi-step reasoning flows. :contentReference[oaicite:3]{index=3}
3. RAG & Knowledge Bases — index, retrieve, and ground responses securely. :contentReference[oaicite:4]{index=4}
4. Fine-tuning & Customization — safe fine-tuning patterns and evaluation metrics. :contentReference[oaicite:5]{index=5}
5. Security & Secrets — secure API keys, STS tokens, and prompt-injection defenses. :contentReference[oaicite:6]{index=6}
6. Deploy & Monitor — scaling, logging, and cost-aware deployment patterns.
7. Integration Lab — build a sample app end-to-end (frontend + Bedrock backend).

Requirements (Prerequisites)

• Familiarity with Python and JavaScript (Node.js recommended).
• Basic ML/MLops knowledge (model inference, HTTP APIs).
• An AWS account (recommended for labs) and basic AWS console experience.
• Willingness to follow hands-on labs and practice secure credential handling.

Learning Outcomes

By the end of this course, you will confidently design generative AI solutions using Amazon Bedrock, deploy agents, protect sensitive data in RAG workflows, and apply security best practices for production-ready systems. Additionally, you will have a working sample application and reproducible lab notebooks.

About the Publication / Instructor Bio

This course was developed by industry practitioners with experience deploying generative AI at scale on AWS. The authors combine cloud architecture experience and applied ML engineering to produce pragmatic patterns and reusable code. Consequently, learners receive not only conceptual material but also deployable templates and secure-by-design recommendations.

Explore These Valuable Resources.

• Amazon Bedrock product page — learn about Bedrock capabilities and supported foundation models.
• Amazon Bedrock documentation — API references, AgentCore docs, and developer guides.
• Hugging Face Transformers — text generation guides and generation strategies for fine-grained control.

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Why Enroll?

Whether you want to prototype a new agent or harden a mission-critical RAG pipeline, this course gives you step-by-step guidance and actionable code. Also, you will receive lab notebooks and deployment templates so you can reproduce each lab immediately.

Additional Notes

All code samples follow secure credential patterns and recommend temporary credentials (AWS STS) for production use; these recommendations align with recent AWS guidance. For security-related best practices and prompt-injection defenses, please consult the included resources and the AWS security blog posts linked above. :contentReference[oaicite:7]{index=7}