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Improving Zero-Shot Ranking with Vespa Hybrid Search
If you are planning to implement search functionality but have not yet collected data from user interactions to train ranking models, where should you begin?
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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.
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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
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