Welcome to the AI Engineering on AWS course repository! This comprehensive course focuses on cutting-edge AI techniques, including LangChain integration with Amazon Bedrock and Multimodal Retrieval-Augmented Generation (RAG). Designed for AI enthusiasts and professionals, this course covers advanced techniques in RAG, Multimodal RAG, and model fine-tuning using Amazon Bedrock.
This course dives deep into the world of AI engineering, leveraging the power of AWS to build sophisticated AI systems. With a strong focus on Multimodal Retrieval Augmented Generation (RAG), you'll learn to:
- Implement advanced RAG techniques using LangChain, LlamaIndex and Unstructured
- Use different foundational models with Amazon Bedrock
- Create powerful multimodal AI systems capable of processing both text and image data
- Explore cutting-edge Multimodal RAG techniques for enhanced information retrieval and generation with OpenSearch
- Fine-tune large language models for specific tasks and domains with Amazon Bedrock
- Implement practical Multimodal RAG applications using AWS services and tools, like Lambda, API Gateway, DynamoDB, and more
- Evaluate and optimize AI model performance for production environments
Our course emphasizes hands-on experience with Multimodal RAG, enabling you to build context-aware AI systems that can understand and process both textual and visual information effectively.
This course covers the following areas:
- LangChain with Amazon Bedrock
- Retrieval Augmented Generation (RAG) with Amazon Bedrock
- Multimodal RAG with Amazon Bedrock
- Fine-tuning with Bedrock
- Model Evaluation with Retrieval Augmented Generation Evaluation Suite (RAGAS)
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✅ Introduction to LangChain with Amazon Bedrock
Explore the fundamentals of LangChain and its integration with Amazon Bedrock. Learn to build conversational AI and question-answering systems. Hands-on examples using Amazon Bedrock's powerful language models, and discover how to chain multiple AI tasks for complex workflows. -
✅ Multimodal RAG with Amazon Bedrock
Learn how to process and analyze both text and image data. Explore advanced multimodal search and retrieval techniques, implement Multimodal Retrieval Augmented Generation (RAG) using AWS services, and develop a practical multimodal application for personalized recommendations.
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Introduction to RAG and Advanced RAG with LlamaIndex
- Understand the principles of Retrieval-Augmented Generation
- Implement advanced RAG techniques using LlamaIndex
- Learn to create knowledge bases and efficient retrieval systems
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Fine-tuning
- Learn the fundamentals of model fine-tuning
- Understand when and how to fine-tune pre-trained models
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Fine-tuning with Bedrock
- Leverage Amazon Bedrock for efficient model fine-tuning
- Optimize models for specific tasks and domains
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Model Evaluation with RAGAS
- Introduction to RAGAS (Retrieval Augmented Generation Evaluation Suite)
- Implement comprehensive evaluation metrics for RAG systems
- Learn to interpret RAGAS scores and improve your RAG pipeline
- Hands-on exercises to evaluate and optimize your RAG models
- Amazon Web Services (AWS): Cloud platform providing a wide range of AI and machine learning services
- Amazon Bedrock: Fully managed service for building generative AI applications
- LangChain: Framework for developing applications powered by language models
- LlamaIndex: Data framework for building LLM applications
- OpenSearch: Search and analytics suite for building scalable solutions
- Unstructured: A platform that provides tools and APIs to preprocess and structure unstructured data like documents, images, and text for easier analysis and machine learning applications.
- Amazon Bedrock: Fully managed service that offers a choice of high-performing foundation models
- Jupyter Notebooks: Interactive computing environment for creating and sharing documents with live code
- RAGAS: Evaluation framework for Retrieval Augmented Generation systems
By the end of this course, you will be able to:
- Design and implement advanced RAG systems using state-of-the-art techniques
- Develop Multimodal RAG applications that process both text and image data
- Leverage AWS services for building scalable AI solutions
- Develop multimodal AI applications capable of processing and generating various data types
- Fine-tune large language models to achieve superior performance on specific tasks
- Leverage Amazon Bedrock for efficient model deployment and scaling
- Implement comprehensive evaluation strategies using RAGAS to assess and improve RAG system performance
- Build production-ready AI systems that can be deployed in real-world scenarios
- Understand and apply best practices for responsible AI development and deployment
- Clone this repository:
git clone https://github.com/debnsuma/fcc-ai-engineering-aws.git - Set up your AWS account and configure your credentials
- Install the required dependencies:
pip install -r requirements.txt - Open the Jupyter notebooks and start exploring!
This repository is actively being updated as the course progresses. We regularly add new content, examples, and exercises to enhance your learning experience. Stay tuned for more exciting updates!
We welcome contributions, issues, and feature requests! If you have suggestions for improving the course content or spot any errors, please don't hesitate to open an issue or submit a pull request. Check out our issues page for current topics.
This project is MIT licensed. Feel free to use, modify, and distribute the content while providing appropriate attribution.
Happy learning, and welcome to the exciting world of AI engineering with AWS!