📉 Turn Your Multimodal Data Into Something You Can Actually Query

Why It Matters

Enterprises increasingly rely on unstructured media, and the ability to index and query that data unlocks new analytics and AI use cases, driving productivity and insight across the organization.

Key Takeaways

• Course teaches OCR and ASR to convert media to text
• Shows Vision Language Model generating timestamped video descriptions
• Builds multimodal RAG retrieving slides, audio, video with citations
• Embeds all modalities into shared vector space for cross-modal search
• Partnered with Snowflake for scalable, governed data pipelines

Pulse Analysis

The explosion of visual and auditory content—photos, recordings, and video—has outpaced traditional data pipelines, which still assume tabular or textual inputs. By converting each modality into structured text, organizations can feed richer signals into large language models (LLMs), improving downstream tasks such as summarization, sentiment analysis, and automated reporting. The new Building Multimodal Data Pipelines course demystifies this process, teaching practical OCR techniques for image extraction and state‑of‑the‑art automatic speech recognition (ASR) for audio, ensuring that raw media become searchable transcripts.

Beyond basic transcription, the curriculum introduces a Vision Language Model (VLM) workflow that produces timestamped descriptions directly from video streams. This enables granular indexing of visual events, allowing users to retrieve specific moments without watching entire recordings. Coupled with a multimodal Retrieval‑Augmented Generation (RAG) system, the course shows how to pull relevant information from slides, audio, and video in a single query, complete with citations—a critical feature for compliance‑heavy sectors like finance and healthcare.

Embedding all modalities into a unified vector space is the linchpin of cross‑modal search. By representing text, image captions, and audio transcripts as vectors, similarity search can span media types, unlocking use cases such as meeting‑recap generation, content recommendation, and knowledge‑base enrichment. Snowflake’s cloud data platform provides the scalability and governance needed for enterprise‑grade pipelines, ensuring data security while handling petabyte‑scale workloads. Professionals who complete the course will be equipped to build end‑to‑end systems that turn multimodal chaos into actionable intelligence, positioning their firms at the forefront of AI‑driven data strategy.

Original Description

Learn more: https://bit.ly/3QcAj29

Images, audio, and video now make up a large share of the data teams work with, but most pipelines still assume everything is structured.

Our latest course, Building Multimodal Data Pipelines, shows how to build pipelines that process multimodal data and turn it into LLM-ready text you can search, analyze, and use in applications.

Built in collaboration with Snowflake and taught by Gilberto Hernandez, this course will teach you how to handle each modality and bring them together into a single system.

What you’ll build:

- Pipelines that convert images and audio into structured text using OCR and ASR

- A Vision Language Model workflow that generates timestamped descriptions from video

- A multimodal RAG system that retrieves across slides, audio, and video to answer questions with citations

Along the way, you’ll see how to embed all modalities into a shared vector space, enabling cross-modal search and retrieval over real-world datasets like meeting recordings.

Enroll now: https://bit.ly/3QcAj29