Embedding models are at the core of search, recommendation, and retrieval-augmented generation (RAG) systems, transforming data into meaningful representations. We can adapt state-of-the-art large language models (LLMs) into embedding models that generate high-quality embeddings, but deploying these models in large-scale applications presents significant challenges.

This talk explores the end-to-end lifecycle of embedding systems, including:

• Leveraging LLMs for high-quality embeddings and adapting them for domain-specific use cases using contrastive learning.
• Designing custom architectures optimized for use-case specific serving requirements.
• Distilling large embedding models into smaller, production-friendly sizes.
• Serving embeddings efficiently with optimization strategies like variable batch sizes and post-training quantization.

Attendees will leave with practical strategies for scaling embedding models from research to production, ensuring high performance and efficiency in real-world applications like retrieving best matching documents, passages or images, data de-duplication, generating personalized recommendations, content clustering, and grounding GenAI responses using RAG approach.