On-demand webinar

Smarter savings on generative AI: cut costs, not innovation

Balance AI Innovation, Productivity, and Costs

The recent generative AI hype has business leaders in a conundrum: How can organizations unlock the full potential of AI investments while keeping costs in check?

This demanding technology amplifies common productivity barriers like data silos, infrastructure constraints, and governance controls.

It's high time to unlock the full potential of data and data science teams. Isn't it time to find a platform that actually saves you money?

Listen to our fireside chat to learn from leaders at Capgemini, NVIDIA, and Domino about approaches that increase team productivity and impact while addressing the need to tighten belts. We also explore how Domino’s Enterprise AI Platform delivers an astounding 722% ROI over three years.

Here's what you'll learn from this on-demand webinar recording:

Transform Data Science into a Profit Center
Increase Productivity and Shrink Costs by 40%
Right-Size Resources, Spend Wisely

Don’t miss this opportunity to drastically reduce costs while amplifying innovation and productivity in data science. Whether you are a data scientist, IT professional, or decision-maker, this webinar is your gateway to transformative strategies for smarter AI investments.

Meet the Speakers

John Gentry

Global DGX Technical Sales and Strategy

John is an enthusiastic technologist and change agent. On the Global DGX Platform Team at NVIDIA, John works with the world's most advanced organizations to design and implement advanced AI systems to solve complex challenges. With over 25 years of Enterprise IT industry experience, from CTO to senior sales engineering, alliances, and marketing roles, John is an expert in understanding key IT industry trends impacting investments for leveraging technology at scale.

Paul Intrevado

San Francisco Practice Lead - Data Science & AI

Paul is a skilled data science manager with 10+ years of experience in the optimization, statistical machine learning, and analytics space. He has the technical knowledge to take deep dives into complex data science problems while having the wherewithal, business acumen, and executive communications skills necessary to drive business solutions. As a former Director and University Professor of Data Science, he has managed projects at over 40 public and private-sector clients across industries spanning tech, service operations, and manufacturing, regularly employing optimization, machine learning, natural language processing and deep learning techniques. As a data science leader, he is able to clearly articulate business requirements and manage data science projects from conception to delivery, assembling teams of highly skilled data scientists to deliver production-ready solutions that drive bottom-line results.