Vector Similarity Search | Future of Data and AI Panel | Conference

Let’s face it, the challenge of search today is indexing billions of entries while delivering relevant results quickly. Traditional keyword-based methods have limitations, leaving us searching for a better way to improve search. But what if we could use deep learning to revolutionize search? Imagine representing data as vectors, where the distance between vectors reflects similarity, and using Vector Similarity Search algorithms to search billions of vectors in milliseconds. It’s the future of search, and it can transform text, multimedia, images, recommendations, and more. 💼 Learn to build LLM-powered apps in just 40 hours with our Large Language Models bootcamp: https://hubs.la/Q01ZZGL-0 Check out this panel discussion, where you will learn how you can incorporate vector search and vector database into your own applications to harness deep learning insights at scale. -- About the Speakers: Sam Partee - Principal Applied AI Engineer at Redis Sam is a Principal Engineer who helps guide the direction of AI efforts within Redis. Sam assists customers and partners in deploying Redis in ML pipelines for feature storage, search, and inference workloads. His background is in high-performance computing and machine learning systems. Harmke Alkemade - AI Cloud Solution Architect at Microsoft Harmke Alkemade is a Specialized AI Cloud Solution Architect at Microsoft, working in a global team that supports customers in various industries to use Azure services for high-impact AI use cases. Previously, she worked as a Cloud Solution Architect for Microsoft Netherlands in the Data & AI domain. Jacky Koh - Founder at Relevance AI Jacky Koh is the founder of Relevance AI, a low-code platform to helps everyone analyze unstructured data 10x faster with embeddings and large language models. Previous to Relevance, Jacky was an app builder and award-winning data scientist, leading teams to deploy impactful machine learning algorithms. Daniel Svonava - Co-Founder at Superli