Vector Databases : Accelerating Learning and Discovery
If you are getting tired of the appearance of new types of databases… too bad. We are increasingly relying on a variety of data storage and retrieval systems for specific purposes. Data does not have a single shape and indexing strategies that work for one are not necessarily good fits for others. So after hierarchical, relational, object, graph, columnoriented, document, temporal, appendonly, and everything else, get ready for Vector Databases to assist in the systematization of machine learning systems.
This will be an overview of the benefits of vectors databases as well as an introduction to the major players.
We will focus on open source versus commercial players, hosted versus local deployments, and the attempts to add vector search capabilities to existing storage systems.
We will cover:
- A brief overview of vectors
- Why vectors are so important to machine learning and datadriven systems
- Overview of the offerings
- Adding vector search to other systems
- Sample use cases shown with one of the key open source engines
Zero fluff.