Abstract: In machine learning, e.g. recommendation tools or data classification, data is often represented as high-dimensional vectors. These vectors are stored in so-called vector databases. With vector databases you can efficiently run searching, ranking and recommendation algorithms. Therefore, vector databases became the backbone of ML deployments in industry.
This session is all about vector databases. If you are a data scientist or data/software engineer this session would be interesting for you. You will learn how you can easily run your favourite ML models with the vector database Weaviate. You'll get an overview of what a vector database like Weaviate can offer: such as semantic search, question answering, data classification, named entity recognition, multimodal search, and much more. After this session, you are able to load in your own data and query it with your preferred ML model!
Session Outline
Lesson 1: What is a vector database?
You'll learn the basic principles of vector databases. How data is stored, retrieved, and how that differs from other database types (SQL, knowledge graphs, etc).
Lesson 2: Performing your first semantic search with the vector database Weaviate.
In this phase, you will learn how to set up a Weaviate vector database, how to make a data schema, how to load in data, and how to query data. You can follow along with examples or you can use your own dataset.
Lesson 3: Advanced search with the vector database Weaviate.
Finally, we will cover other functionalities of Weaviate: multi-modal search, data classification, connecting custom ML models, etc.
Background Knowledge
This is an introduction to Vector databases, but basic knowledge of Python and machine learning are preferred.
Bio: Laura is a Data Scientist at SeMI, where we build 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.
