GPU-accelerated Data science with the kinetica active analytics platform
DATE / TIME: Wednesday, December 11 at 10:00am PT
To help organizations incorporate machine learning into their active analytical applications, we recently announced new additions to our Active Analytics Platform that democratize access to data science and expand our integration with the popular RAPIDS GPU-accelerated open source library. With Kinetica you can optimize your predictive analytics capabilities and promote data-driven decision-making across your organization.
IN THIS WEBINAR, WE'LL EXPLORE:
MEET THE HOSTS:
Irina Farooq - Chief Product Officer, Kinetica
Irina has over a decade of product management experience across a variety of sectors, including enterprise software, networking, hardware, IoT, SaaS, and Cloud. Irina joins Kinetica from Riverbed Technology, where she held a variety of leadership roles including Vice President of Products and Strategy for the Service Provider Business and Vice President of Product Management for Steelhead, Riverbed's flagship product. Prior to Riverbed, Irina was Vice President of Embedded Systems for Grid Net, a Smart Grid/IoT company, where she was responsible for engineering and product management of the company's hardware and firmware. Irina started her career as a software engineer and product manager at Oracle. Irina holds a B.S. in Mathematics and a B.S. in Computer Science from MIT, an M.B.A. from Stanford Graduate School of Business, and an M.S. in Environment and Resources from Stanford University.
Saif Ahmed - Product Owner-Machine Learning, Kinetica
Saif Ahmed is an accomplished quantitative developer, machine learning practitioner, and senior executive with two decades of experience in management consulting, quantitative hedge funds, and AI software startups. He has held a number of senior roles and serviced clients across the US, Europe, the Middle East and Asia. Saif is currently applying his experience at Kinetica, leading ML Product Engineering efforts for a next-generation Machine Learning product line with database adjacency.