Amazon Bedrock, Amazon Q & AWS Generative AI [2025]

It looks like you've outlined a comprehensive curriculum for a course on leveraging generative AI, specifically focusing on Amazon Bedrock and other AWS services in conjunction with various foundation models. The course is structured to cover both theoretical knowledge and practical application, with a variety of use cases and hands-on exercises.

Here's a summary of the course structure:

1. Introduction to Generative AI and AWS Bedrock - Overview of generative AI capabilities, introduction to Amazon Bedrock, and setup of development environment.

2. Amazon Bedrock Architecture Explained - Detailed explanation of the architecture behind Amazon Bedrock, including its components and how it integrates with other services.

3. AWS Bedrock Pricing - Understanding the pricing model for using Amazon Bedrock.

4. Inference Parameters in AWS Bedrock - Learning about the different parameters that influence the inference process on Amazon Bedrock.

5. Use Case 1: Media and Entertainment Industry - Building a system to generate movie poster designs using API Gateway, S3, and the Stability Diffusion Foundation Model.

6. Use Case 2: Text Summarization for Manufacturing Industry - Implementing text summarization tools for the manufacturing sector using API Gateway, S3, and the Cohere Foundation Model.

7. Build a Chatbot with Amazon Bedrock (Llama 2) - Creating a chatbot leveraging the Llama 2 foundation model along with Langchain and Streamlit.

8. Use Case 4: Employee HR Q&A Application with Retrieval Augmented Generation (RAG) - Developing an HR Q&A application using Amazon Bedrock (Claude FM), Langchain, FAISS, and Streamlit.

9. Serverless e-Learning App with Amazon Bedrock Knowledge Base - Building a serverless e-learning app using the Bedrock Knowledge Base, Claude FM, AWS Lambda, and API Gateway.

10-14. GenAI Project Lifecycle: Phases 1-4 - A deep dive into the different phases of a generative AI project lifecycle, including use case selection, foundation model selection, prompt engineering, fine-tuning of models, and code generation using AWS CodeWhisperer.

Throughout the course, various foundational concepts are refreshed, such as Python basics, AWS Lambda, API Gateway, and the principles of AI, ML, and artificial neural networks. The course also covers prompts design techniques for Claude, Titan, and Stability AI foundation models and evaluates foundation models with hands-on activities.

The services used in this course include:

• Amazon Bedrock
• Llama 2 Foundation Model
• Cohere Foundation Model
• Stability Diffusion Model
• Claude Foundation Model from Anthropic
• Langchain - Chains and Memory Modules
• FAISS Vector Store
• AWS CodeWhisperer
• API Gateway, Lambda, Streamlit, S3, Prompt design Techniques for LLMs, Fine Tuning of Foundation Models, Python, Evaluation of Foundation Models, AI, ML, Artificial Neural Networks, and Generative AI.

This curriculum is designed to provide a solid understanding of the technologies involved in creating generative AI applications on AWS, with a focus on Amazon Bedrock as a key platform for deploying these models. It aims to equip learners with both the theoretical background and practical skills needed to work on real-world projects involving generative AI.