Building Multimodal Search and RAG

Course Description

This course provides an in-depth exploration into multimodal AI technologies, focusing on contrastive learning to build modality-independent embeddings for advanced retrieval systems, and on developing multimodal Retrieve and Generate (RAG) systems. You will learn to implement practical applications of multimodal search and construct multi-vector recommender systems that enhance user experiences across various industries.

What Students Will Learn

• Understanding and building multimodal search and RAG systems that integrate different types of data such as text, images, audio, and video.
• Training multimodal models using contrastive learning and applying these models to real datasets.
• Developing techniques for any-to-any multimodal search to retrieve relevant information across disparate data types.
• Applying visual instruction tuning to train Large Language Models (LLMs) that process and interpret multimodal data.
• Implementing an end-to-end multimodal RAG system capable of generating insightful responses from analyzed multimodal contexts.
• Exploring real-world applications, like analyzing visual documents to extract structured data, and building multi-vector recommender systems.

Prerequisites

Participants should have a basic understanding of Python and familiarity with RAG concepts. These prerequisites are essential for engaging effectively with the course content and building the discussed systems.

Course Content Overview

• Introduction to multimodal AI and contrastive learning fundamentals.
• Hands-on implementation of multimodal search and RAG systems.
• Techniques for training LLMs with visual instruction tuning for multimodal comprehension.
• Development of an integrated multimodal RAG system from start to finish.
• Industry-specific applications, including visual data analysis and multi-vector recommender systems.

Who This Course Is For

This course is designed for developers, AI researchers, and technical product managers who are interested in advancing their skills in multimodal AI technologies. It is especially beneficial for those planning to develop or enhance applications that necessitate the integration and analysis of diverse data types.

Real-World Applications

The skills taught in this course can be applied in various domains, including e-commerce, for improving product recommendations through multi-vector systems that assess similarity across different modalities. In customer service, AI can leverage multimodal data to provide more accurate and context-aware responses. Additionally, in any sector where data comes in varied forms, such as healthcare or public safety, these skills enable the creation of more robust and efficient analysis and retrieval systems.