All Stories
Preview of Vespa on ARM64
With the increasing adoption of ARM64 based hardware like the AWS Graviton and Apple M1 MacBooks we are making a preview of Vespa available for this architecture.
Vespa Newsletter, January 2022
Advances in Vespa features, performance and operability improvements include: Improved synonym support, faster node recovery, re-balancing and re-indexing, WeakAnd query type and new pyvespa features and sample applications.
Billion-scale vector search with Vespa - part two
Part two in a blog post series on billion-scale vector search with Vespa. This post explores the many trade-offs related to nearest neighbor search.
Doubling the throughput of data redistribution
Learn which improvements we made to double the throughput of data redistribution in Vespa.
Machine-learned model serving at scale
An under-communicated point is that the default tuning for most libraries and platforms for evaluating machine-learned models is unsuitable for serving at scale.
Vespa Newsletter, December 2021
Advances in Vespa features and performance include tensor performance improvements, match-features and a Vespa IntelliJ plugin.
Log4j vulnerability update (CVE-2021-44228)
Brief update on our current knowledge about the Log4j vulnerability (CVE-2021-44228) and Vespa.
Basic HTTP testing of Vespa applications
Write basic tests for a Vespa application in JSON format, run them with the Vespa CLI, and use them to verify production deployments in Vespa Cloud.
Billion-scale vector search with Vespa - part one
Part one in a blog post series on billion-scale vector search. This post covers using nearest neighbor search with compact binary representations and bitwise hamming distance.
Text-to-image search with Vespa
In this post we explore a text-to-image search application on Vespa using approximate nearest neighbor search on vector representations of text and images.
Vespa Newsletter, November 2021
Advances in Vespa features and performance include improved schema inheritance, Intellij plugin for schemas, Hamming distance in ranking, and performance gains in data dump and application deployment.
Never miss a story from us, subscribe to our newsletter