Fine-tuning Large Language Models (LLMs) is already powerful—but what if you could do it using multi-modal, unstructured data like images, videos, PDFs, or sensor feeds?
In this joint session by Dataloop and SingleStore, learn how to fine-tune LLMs on real-world, multi-modal datasets using Dataloop’s unstructured data pipeline and SingleStore’s hybrid search capabilities (combining full-text and vector search in one engine). We'll show how this integration enables scalable AI pipelines that can reason across formats—images, documents, metadata, and more.
Expect a live demo, real-world use cases, and clear strategies for operationalizing AI on messy, real-world data.
- How to organize and annotate multi-modal datasets using Dataloop
- How SingleStore’s hybrid search (text + vector) enables fine-tuning and retrieval
- Best practices for aligning LLMs with unstructured enterprise data
- A live demo: fine-tuning on unstructured data and performing semantic + keyword search
