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Live Webinar

How Speed Drives Actionable Insights with Numbers Station

Wednesday, October 9th, 2024 at 1pm ET/10am PT

Join us for the upcoming webinar, "How Speed Drives Actionable Insights with Numbers Station," presented by Numbers Station and SambaNova.

As the pace of agent development accelerates, many solutions are still struggling to move from theory to production. This session will focus on how emerging Large Language Models (LLMs) and rapid inference are transforming multi-agent architectures and powering the advanced Retrieval-Augmented Generation (RAG) pipeline at the heart of the Numbers Station data intelligence platform.

Through a combination of structured and unstructured data, learn how to generate actionable insights by leveraging a network of agents. The webinar will offer a deep dive into the mechanics behind hyper-specialized data agents and how they can collaboratively solve complex data tasks within an enterprise setting. Plus, discover why speed is a critical factor in creating a cutting-edge RAG pipeline.

Reserve Your Spot

Don't miss the opportunity to learn how speed and precision are transforming the world of multi-agent data architectures and empowering enterprises to take real actions from insights!
vasanth-mohanYour Host

Vasanth Mohan

Director, Technical Product Marketing & Developer Relations, SambaNova

Vasanth Mohan is a leader in AI-driven developer solutions with extensive experience guiding startups and enterprises in implementing cutting-edge technologies. As the Director of Technical Product Marketing and Developer Relations at SambaNova, Vasanth has spearheaded efforts to bring innovative AI solutions into production. His work focuses on enabling developers to leverage Agentic AI for business-critical applications, making him an invaluable resource in understanding how to operationalize these advancements.

ines-chamiGuest Speaker

Ines Chami

Co-Founder & Chief Scientist, Numbers Station

Ines Chami is a co-founder and Chief Scientist at Numbers Station, with a Ph.D. from Stanford University in Computational and Mathematical Engineering. Her groundbreaking research includes applications in knowledge graph construction and data cleaning, and she has received prestigious recognition, including the Stanford Gene Golub Doctoral Dissertation Award. Ines brings deep expertise in building intelligent systems that simplify data-heavy processes, making her insights invaluable for those looking to build practical and scalable AI-driven solutions.