Abstract: 

Today, we've gotten used to natural language search and recommendation systems. We expect to get what we search for without remembering the exact keyword. To tackle this, we use ML models that create vectors which represent the semantics of data, and a way to efficiently store and retrieve large amounts of vector and non-vector data. Scaling ML models to work reliably in production is hard and implementing efficient vector search while keeping real-time CRUD support that is expected from databases is even harder.

Vector search engines form a solution to these challenges. A vector search engine helps you create machine-learning-first search and recommendation systems based on your data. It searches through your data super-fast with Approximate Nearest Neighbor (ANN) search, while also supporting all CRUD operations and data mutability.

In this session, you will learn what vector search is, why and when you would need it and you will see vector search in action during live demos.

Bio: 

Zain Hasan is a Senior Developer Advocate at SeMI Technologies - the company behind the Weaviate vector search engine. He is an engineer and data scientist by training, who pursued his undergraduate and graduate work at the University of Toronto building artificially intelligent assistive technologies for elderly patients. He then founded his company developing a digital health platform that leveraged machine learning to remotely monitor chronically ill patients using data from their medical devices. More recently he practiced as a consultant senior data scientist in Toronto. He is passionate about the field of data science and machine learning and loves to share his love for the field with anyone interested in the domain.