Presentation slides derived from: https://www.semi.technology/developers/weaviate/current/#introduction-video
Vector search engines like Weaviate are the new kid on the block in the database landscape. The term "vector search engine" might be new to you, but there's a good chance you've used one today; the most famous example of a vector search engine in action is Google Search.
With Weaviate, SeMI aims to make the power of machine learning and graphs in the cloud available to business users so that they can better search through, classify and generate data inside their existing data landscape. Join us for a walk on the AI side, and see how you can use open source for fun and profit.
We'll look at Weaviate's core functions - similarity search, automatic classification, and data enrichment. We'll see how it exposes the insights through its graph-like data model and API, and how customers implement its features in day-to-day operations.
We'll talk about use cases in Fast-Moving Consumer Goods, finance, e-commerce, and cybersecurity. Last but not least, we'll do a deep dive into how Weaviate is used in retail to automatically relate customers from different ERP systems and public datasets.
Also check: https://www.zdnet.com/article/weaviate-an-open-source-search-engine-powered-by-machine-learning-vectors-graphs-and-graphql/