Enterprise AI Tutorial - Embeddings, RAG, and Multimodal Agents Using Amazon Nova and Bedrock

Explore comprehensive AI engineering concepts through a 5-hour 36-minute tutorial covering embeddings, retrieval-augmented generation (RAG), multimodal models, and intelligent agents using Amazon Nova and Bedrock services. Master fundamental natural language processing concepts starting with embeddings in NLP and large language models, then dive deep into Byte-Pair Encoding (BPE) tokenization techniques. Learn to implement Amazon Tian Text Embeddings for semantic search and text understanding applications. Discover multimodal large language models and their capabilities, including detailed exploration of Contrastive Language-Image Pre-training (CLIP) and Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models (BLIP-2) architectures. Work hands-on with Amazon Nova's multimodal model capabilities for processing both text and visual content. Build practical multimodal RAG systems that can retrieve and generate responses using multiple data types. Construct end-to-end applications utilizing Amazon Bedrock Agents and Knowledge Bases, with real-world implementation focused on automating insurance claim processes. Gain experience with LangChain integration with Amazon Bedrock for building sophisticated AI workflows. Learn to leverage Bedrock-powered agents for tasks including claim creation, document management, and data retrieval to enhance efficiency and decision-making in customer service operations. Access accompanying GitHub resources and practical code examples throughout the learning experience.