📉 Turn your multimodal data into something you can actually query

2026-04-22 · Source: DeepLearningAI · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Intermediate, quick

Summary

A new course, developed in partnership with Snowflake and instructed by Gilberto Hernandez, focuses on building multimodal data pipelines for RAG applications. The course teaches participants to create an application that answers questions by searching across audio, images, and video, utilizing real-world meeting recordings. It covers integrating AI-based multimodal techniques, including automatic speech recognition for audio-to-text conversion, image-to-text description generation, and vision-language models for video segment descriptions. The curriculum culminates in creating embeddings from extracted text and implementing a RAG application powered by these pipelines, enabling detailed answers and contextual tracing across scenes over time.

Key takeaway

For AI Engineers building advanced RAG applications, this course offers a practical guide to integrating multimodal data. You will learn to process audio, image, and video content into a unified search index, significantly enhancing contextual understanding and answer quality for complex queries. Consider this course to expand your application's data accessibility beyond traditional text sources.

Key insights

Multimodal data pipelines enable RAG applications to query information across audio, images, and video.

Principles

• Combine modalities for rich context
• Trace events across scenes over time

Method

Apply ASR for audio, convert images to text, use vision-language models for video, then create embeddings for RAG.

In practice

• Build RAG for meeting recordings
• Generate descriptions from video segments

Topics

• Multimodal Data Pipelines
• RAG Applications
• Automatic Speech Recognition
• Vision-Language Models
• Data Embeddings

Best for: AI Engineer, Machine Learning Engineer, AI Student

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Editorial summary, takeaway, and curation by AIssential. Original article published by DeepLearningAI.