Abstract: In machine learning – like recommendation tools or data classification – data is often represented as high-dimensional vectors. These vectors are stored in so-called vector databases. Vector databases are the backbone of ML deployments in industry, they are designed and optimized to run search, ranking and recommendation algorithms.
If you are a data scientist or a data/software engineer join Laura to learn how to run your favorite ML models with a vector database like Weaviate. But also to learn about other features like: semantic search, question answering, data classification, named entity recognition, multimodal search, that you should expect from a Vector Database.
Finally, Vector search will be illustrated with live demos of a real use case! After this session, you will know when and how to use Vector Search with various ML models.
Bio: Laura is a ML Product Researcher at SeMI Technologies, the company behind the open-source vector search engine Weaviate. She researches new machine learning features for Weaviate and works on everything UX/DX related to Weaviate. For example, she is responsible for the GraphQL API design. She is in close contact with our open source community. Additionally, she likes to solve custom use cases with Weaviate, and introduces Weaviate to other people by means of Meetups, talks and presentations.
Data Scientist | SeMI Technologies