Making, Moving, and Managing Millions of Embeddings

AI applications thrive on large amounts of embedding data. But as the number of embeddings grows, so do the challenges. One of the most significant challenges is generating and indexing vector embeddings at scale — from tens to hundreds of millions. Cracking this challenge makes the difference between building a “fun demo” and a production-quality AI application that catapults you past the competition.

In this session, we’ll explore how to tackle this challenge using Pinecone and Spark (with Databricks). We’ll discuss various techniques for producing vector embeddings and using the Pinecone Spark connector to load the embeddings into Pinecone. We’ll take a look at how to manage the lifecycle of our embeddings and cover some of the challenges and pitfalls that users may run into when managing vector embeddings at scale and how to overcome them.