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Photo by Niels Weiss on Unsplash
Improving Product Search with Learning to Rank - part three
This is the third blog post on applying learning to rank to enhance E-commerce search.
Photo by Ilya Pavlov on Unsplash
Vespa Newsletter, November 2022
Vespa features and performance advances include ANN Pre-Filter Performance, Parent Field Hit-Estimates, Model Training Notebooks, and GCP Support.
Photo by Carl Campbell on Unsplash
Improving Product Search with Learning to Rank - part two
This is the second blog post on applying learning to rank to enhance E-commerce search.
Vespa Cloud on Google Cloud Platform
Vespa Cloud is now available on Google Cloud Platform
Photo by Pawel Czerwinski on Unsplash
Improving Product Search with Learning to Rank - part one
This is the first blog post on applying learning to rank to enhance E-commerce search.
Photo by Scott Graham on Unsplash
Vespa Newsletter, October 2022
Advances in Vespa features and performance include a BertBase Embedder / model hub, improved query performance, paged attributes, ARM64 support and term bolding in string arrays.
Photo by julien Tromeur on Unsplash
Building Billion-Scale Vector Search - part two
Searching billion-scale datasets without breaking the bank.
Vespa on ARM64
Vespa is now released as a multiplatform container image, supporting both x86_64 and ARM64.
Photo by Arnaud Mariat on Unsplash
Building Billion-Scale Vector Search - part one
How fast is fast? Many consider the blink of an eye, around 100-250ms, to be plenty fast.
Pre-trained models on Vespa Cloud
Vespa Cloud now provides pre-trained ML models for your applications
Text embedding made simple
Vespa now lets you create a production quality semantic search application from scratch in minutes
Photo by Ilya Pavlov on Unsplash
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