2026-06-08 –, Palais Atelier
Small, domain-specific vision models can dramatically enhance the buyer search experience by delivering more relevant visual understanding. But the real opportunity comes from controllable image embeddings: by fusing base search embeddings with additional control vectors, representing features such as color, shape, and style,
In this talk, we’ll show how Vespa’s ranking engine, advanced fashion visual model, supported by tensor operations enables dense vector–based search tuning for fashion apparel search, making product discovery faster, more intuitive, and more aligned with user preferences. We’ll walk through the configuration examples of how these ranking pipelines are built in Vespa: from schema design and tensor field definitions, to ranking expressions using tensor arithmetic, to practical patterns for production deployment.
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This talk is sponsored by Vespa.ai
Piotr Kobziakowski is a Senior Principal Solutions Architect at Vespa.ai, where he leverages over 20 years of expertise in software architecture, network security, big data, and search technologies to design scalable AI-driven solutions for global enterprises. Based in Warsaw, Poland, he specializes in advising organizations on data, analytics and search applications.
Prior to joining Vespa.ai in October 2024, Kobziakowski held progressive technical roles at Elastic, where he architected search and analytics solutions for telecommunications. His career spans across industry leaders like Akamai, Nominum, Cloudmark and Bytemobile, with a focus on optimizing large-scale data and analytics infrastructure and security systems.
Piotr’s approach combines hands-on technical advisory with strategic problem-solving,
through delivering workshops and customized training programs. He is recognized for translating complex technical concepts into actionable roadmaps, enabling enterprises to operationalize technology capabilities. A frequent speaker at many events related to GenAI, Data and Analytics.