Overview
GPU scarcity, escalating costs, and a fragmented infrastructure are significant roadblocks for organizations striving to leverage the power of Artificial Intelligence and Machine Learning. Are you struggling with limited GPU access, leading to inefficient workflows and potential security risks? Is your valuable GPU capacity hidden in silos, making optimization and cost management a challenge? And how can you confidently enable collaborative AI development in a secure, multi-tenant environment? This webinar will highlight these critical limitations that hinder AI/ML progress. Join us to discover how these challenges can be overcome, paving the way for streamlined operations, maximized resource utilization, and accelerated innovation in your AI initiatives, with GPU-as-a-Service.
TAKEAWAYS:
- Understand Key GPU Management Challenges: Recognize the significant hurdles of GPU scarcity, cost, fragmented infrastructure, and security in AI/ML
- Grasp the Power of Centralized GPU-as-a-Service: Learn how a unified approach to GPU resources can simplify management and improve accessibility
- Explore Strategies for Maximizing GPU Utilization: Discover the importance of optimizing GPU usage to get the most out of your investment and improve workload performance
Any questions? please contact Elisa Navarro
Martin Isaksson
Lead GTM and Business development EMEA, AI BU, Red Hat
Over the last decade, Martin has been focused on exploring the opportunities that AI and deep learning offers to business leaders. With experience ranging from conducting AI research with Stanford University to leading a Silicon Valley AI startup, Martin now uses his skills to lead Red Hat’s AI go-to-market strategies and business development in EMEA.
