Approximate Vector Search with KMeans and Azure SQL | Data Exposed

From the creator

In this episode, we'll see how to calculate KMeans clusters for vector data so that then it can be used to do Approximate Similarity Search. We'll offload resource intensive processing to calculate KMeans using SciKit-Learn to a container and then we'll do cell probing in pure T-SQL. Chapters: 00:00 - Introduction 02:15 - Vector in SQL 04:00 - Indexing 08:40 - KMeans 11:25 - Demo ✔️Resources: Intelligent applications with Azure SQL Database: https://aka.ms/sqlai Azure SQL Devs’ Corner: https://devblogs.microsoft.com/azure-sql/ Vector Search Optimization via KMeans, Voronoi Cells and Inverted File Index (aka “Cell-Probing”): https://devblogs.microsoft.com/azure-sql/vector-search-optimization-via-kmeans-voronoi-cells-and-inverted-file-index-aka-cell-probing/ 📌 Let's connect: Twitter - Anna Hoffman, https://twitter.com/AnalyticAnna Twitter - AzureSQL, https://aka.ms/azuresqltw 🔴 Watch even more Data Exposed episodes: https://aka.ms/dataexposedyt 🔔 Subscribe to our channels for even more SQL tips: Microsoft Azure SQL: https://aka.ms/msazuresqlyt Microsoft SQL Server: https://aka.ms/mssqlserveryt Microsoft Developer: https://aka.ms/microsoftdeveloperyt #AzureSQL #SQL #LearnSQL

Published 2024-04-25 · Watch on YouTube ↗
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