Machine Learning (ML) represents everything as vectors, from documents, to videos, to user behaviors. This representation makes it possible to accurately search, retrieve, rank, and classify different items by similarity and relevance.
However, working with vectors isn’t always easy. Many engineers and data scientists are just starting to understand how to work with vectors, and many software and infrastructure products were not designed to support them.
These guides will help engineers, data scientists, and even executives understand and harness the power of vectors to build better applications for their business.