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

Ensure high-accuracy, efficient vector search at massive scale
with Azure Cosmos DB. Leveraging Microsoft’s DiskANN, more IO
traffic moves to disk to maximize storage capacity and enable
high-speed similarity searches across all data, reducing memory
dependency. This technology, powering global services like
Microsoft 365, is now integrated into Azure Cosmos DB, enabling
developers to build scalable, high-performance applications with
built-in vector search, real-time fraud detection, and robust
multi-tenancy support.


Join Kirill Gavrylyuk, VP for Azure Cosmos DB, as he shares how
Azure Cosmos DB with DiskANN offers unparalleled speed,
efficiency, and accuracy, making it the ideal solution for modern
AI-driven applications.


 


QUICK LINKS: 
00:00 - Latest Cosmos DB optimizations with DiskANN
02:09 - Where DiskANN approach is beneficial
04:07 - Efficient querying
06:02 - DiskANN compared to HNSW
07:41 - Integrate DiskANN into a new or existing app
08:39 - Real-time transactional AI scenario
09:29 - Building a fraud detection sample app
10:59 - Vectorize transactions for anomaly detection
12:49 - Scaling to address high levels of traffic
14:05 - Manage multi-tenancy
15:35 - Wrap up


 


Link References


Check out https://aka.ms/DiskANNCosmosDB


Try out apps at https://aka.ms/DiskANNCosmosDBSamples


 


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