Vector Search using 95% Less Compute | DiskANN with Azure Cosmos DB

This video introduces the concept of vector search using DiskANN with Azure Cosmos DB, which promises to reduce compute costs by a whopping 95%. Vector search is a ubiquitous technique in the field of machine learning used to find nearest neighbors in high-dimensional spaces. However, the computational demands of vector search can quickly become prohibitive, as the number of dimensions grows.

Enter DiskANN, a technique that leverages a combination of disk I/O and in-memory computations to significantly reduce the computational overheads of vector search. Furthermore, by using Azure Cosmos DB, DiskANN adds an additional layer of scalability and reliability to the equation. This video provides an overview of how DiskANN and Azure Cosmos DB can be combined to perform efficient vector search at scale, with detailed demonstrations and usage guidelines.

If you're a machine learning engineer or data scientist looking for ways to optimize your vector search performance, this video is an excellent starting point. With its clear explanations and comprehensive demonstrations, you'll walk away with a firm understanding of how DiskANN and Azure Cosmos DB can be used to transform your search capabilities while reducing computational overheads by up to 95%.

The video on Vector Search using 95% Less Compute | DiskANN with Azure Cosmos DB can be found on YouTube.