*Voyage AI* is the newest giant in the embedding, reranking, and search model game!<p>I am SUPER excited to publish our latest Weaviate podcast with Tengyu Ma, Co-Founder of Voyage AI and Assistant Professor at Stanford University!<p>We began the podcast with a deep dive into everything embedding model training and contrastive learning theory. Tengyu delivered a *masterclass* in everything from scaling laws to multi-vector representations, neural architectures, representation collapse, data augmentation, semantic similarity, and more! I am beyond impressed with Tengyu's extensive knowledge and explanations of all these topics.<p>The next chapter dives into a case study Voyage AI did *fine-tuning an embedding model for the LangChain documentation.* This is an absolutely fascinating example of the role of continual fine-tuning with very new concepts (for example, very few people were talking about chaining together LLM calls 2 years ago), as well as the data efficiency advances in fine-tuning.<p>We concluded by discussing ML systems challenges in serving an embeddings API. Particularly the challenge of detecting if a request is for batch or query inference and the optimizations that go into either say ~100ms latency for a query embedding or maximizing throughput for batch embeddings.<p>I hope you find the podcast interesting, more than happy to discuss any of these topics with you or answer any questions about the content in the podcast! Thank you so much!<p>YouTube: https://www.youtube.com/watch?v=xPdyivfheqI<p>Spotify: https://spotifyanchor-web.app.link/e/u6XPLYfF7Hb