-
Overview of Vespa.ai
Get a high-level introduction to Vespa.ai. In just two minutes, you’ll understand how Vespa is positioned as a platform built for performance, scalability, and accuracy—core requirements for powering modern AI-driven applications. Ideal for a quick orientation to what sets Vespa apart.
-
Webinar: Unlock the Future of eCommerce – One Platform, Unlimited Possibilities
In this webinar recording, learn how Vespa.ai transforms online retail by combining search, ranking, and recommendations in one scalable platform. See how Vespa’s architecture enabled Vinted to scale to over 1 billion listings, accelerate query speeds, and cut operational costs—all while delivering precise, AI-powered search experiences.
-
Vespa Voice: The Rise of Vector Databases – Breaking Down GigaOm’s Sonar Report
In this episode, we chat with Whit Walters, Field CTO at GigaOm, and dive into GigaOm’s latest Sonar Report on vector databases, exploring why Vespa.ai is recognized as both a Leader and Fast Mover in the space. We break down what sets Vespa apart—from its integrated architecture that runs data, indices, metadata, and machine learning inferences on the same physical nodes, to its unmatched performance at scale.
-
Webinar: Scaling AI Beyond Vectors: Building Smarter, Faster Systems for RAG
During this Data Science Connect webinar, Aakarsh Ramchandani, Chief Strategy Officer, Ravenpack (Bigdata.com) and Jon Bratseth, CEO and Founder of Vespa.ai discuss strategies for leveraging generative AI beyond vector databases. Topics include scaling AI systems for performance, combining machine learning with search capabilities for enhanced retrieval-augmented generation (RAG) and best practices for getting started with generative AI.
-
Webinar: Transforming the Future of RAG with ColPali
During this Data Science Connect webinar, Aakarsh Ramchandani, Chief Strategy Officer, Ravenpack (Bigdata.com) and Jon Bratseth, CEO and Founder of Vespa.ai discuss how ColPali is transforming information retrieval by integrating visual data from multimodal documents like PDFs, unlocking new possibilities for Retrieval-Augmented Generation (RAG). This cutting-edge solution simplifies processing for visually rich documents, ensuring greater accuracy and relevance in AI-driven workflows.
-
TechTalk: Building a Visual RAG application with Vespa in Python
This AI Camp meetup presentation describes how to build an end-to-end Visual RAG application for PDF files using Vespa, with a Python-only approach. Learn practical insights and best practices for developing your own Visual RAG applications, with a focus on scalability and performance. The session also covers promising advancements in document search and retrieval. All referenced code is open source, providing a valuable resource for participants to apply these techniques in their own projects.
-
Webinar: RAG and Roll: Transforming GenAI Use Cases
Watch this on-demand webinar from industry analysts BARC, to explore how Retrieval-Augmented Generation (RAG) provides a simple, cost-effective alternative to fine-tuning GenAI language models for domain-specific data. Learn how RAG works, including its integration with language models and data pipelines, discover common architectural approaches such as vector, relational, and graph RAG, and gain practical insights and guiding principles to get started.
