Zain Hasan

Join the 89th Weaviate Podcast on Matryoshka Embeddings with Aditya Kusupati, Zach Nussbaum, and Zain Hasan. Learn about challenges in training Matryoshka embeddings, experiences building embeddings API, Aditya's research on differentiable ANN indexes, and more!

Zain Hasan, a semantic search and augmented LLMs expert, joins the podcast to discuss the challenges of teaching large language models. They explore the concept of vector databases and their role in enhancing chat bots. The episode delves into optimizing search in a fictional service, the size and storage of indexes in vector databases, and the concept of multi-modality in vector search. The discussion also includes implementing semantic search at home using Weaviate and a conversation on Weaviate, an open-source database with managed instances.

Zain Hasan, a former ML engineer at Weaviate and now a Senior AI/ML Engineer at Together, dives into the fascinating world of vector database quantization. He explains how quantization can drastically reduce storage costs, likening it to image compression. Zain discusses three quantization methods: binary, product, and scalar, each with unique trade-offs in precision and efficiency. He also addresses the speed and memory usage challenges of managing vector data, and hints at exciting future applications, including brain-computer interfaces.