I’m the Founder and CEO of Pinecone, the vector database for machine learning.
Until April 2019, I was a Director of Research at AWS and Head of Amazon AI Labs. The Lab built cutting-edge machine learning algorithms, systems, and services for AWS customers. We build parts of SageMaker, Kinesis, QuickSight, Amazon ElasticSearch, Glue, Rekognition, DeepRacer, Personalize, Forecast, and other yet-to-be-released services.
Before AWS, I was a Senior Research Director at Yahoo and Head of Yahoo’s Research Lab in New York. We worked on building horizontal machine learning platforms and improving applications such as online advertising, search, security, media recommendation, email abuse prevention, and many more.
I received my B.Sc in Physics and Computer Science from Tel Aviv University and my Ph.D. in Computer Science from Yale University. After that, I was a Postdoctoral fellow at Yale in the Program in Applied Mathematics.
May 27, 2021 04:25 PM PT
Modern Machine Learning (ML) represents everything as vectors, from documents, to videos, to user behavior. This representation makes it possible to accurately search, retrieve, rank, and classify different items by similarity and relevance.
Running real-time applications that rely on large numbers of such high dimensional vectors requires a new kind of data infrastructure. In this talk we will discuss the need for such infrastructure, the algorithmic and engineering challenges in working with vector data at scale, and open problems we still have no adequate solutions for.
Time permitting, I will introduce Pinecone as a solution to some of these challenges.