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DeepLearning.AI

Building Multimodal Search and RAG

  • Intermediate

Learn to build advanced AI systems that understand and process multiple data types simultaneously. This course will guide you through the creation of multimodal search and RAG systems, crucial for next-generation AI applications.

  • AI
  • Machine Learning
  • Data Science
  • Information Retrieval
  • Natural Language Processing

Overview

This course provides a comprehensive understanding of building multimodal search and RAG systems, focusing on the integration of various data types like text, images, and video into AI models. You will learn about contrastive learning for training multimodal models, visual instruction tuning, and the implementation of multi-vector recommender systems that enhance the capabilities of AI applications in industry settings.

  • Web Streamline Icon: https://streamlinehq.com
    Online
    course location
  • Layers 1 Streamline Icon: https://streamlinehq.com
    English
    course language
  • Self-paced
    course format
  • Live classes
    delivered online

Who is this course for?

AI Developers

AI developers interested in learning how to build systems that process and reason over multiple data modalities.

Data Scientists

Data scientists looking to enhance their skills in multimodal AI applications.

Technology Enthusiasts

Individuals with a keen interest in the latest AI technologies and their applications in real-world scenarios.

Enhance your AI skills by mastering multimodal systems that integrate and process diverse data types. This course will equip you with the knowledge to build sophisticated search and RAG systems, making you a valuable asset in the evolving field of AI.

Pre-Requisites

1 / 3

  • Basic knowledge of Python

  • Familiarity with RAG systems

  • Interest in AI and machine learning technologies

What will you learn?

Introduction to Multimodal AI
Overview of multimodal AI technologies and their significance in modern AI applications.
Contrastive Learning for Multimodal Models
Detailed exploration of contrastive learning techniques used to train models capable of understanding multiple data types.
Building RAG Systems
Step-by-step guide on developing RAG systems that enhance LLMs by incorporating diverse data modalities.
Implementing Multimodal Search
Techniques for creating effective search systems that operate across various data types.
Visual Instruction Tuning for LLMs
Methods to train LLMs to comprehend multimodal data using visual instruction.
Multi-vector Recommender Systems
Creation of recommender systems that analyze similarities across multiple modalities to suggest relevant items.
Industry Applications
Practical applications of multimodal AI in industries, including analysis of invoices and flowcharts.

Meet your instructor

  • Sebastian Witalec

    Head of DevRel, Weaviate

    Sebastian Witalec is the Head of DevRel at Weaviate. He brings a wealth of experience in the field of artificial intelligence and machine learning to his role.

Upcoming cohorts

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