Eliminate Waste and Lower Cloud Costs for GPU-Accelerated Big Data Applications

Cloud GPUs are quickly becoming mainstream for big data applications like Spark on Kubernetes. Big data companies looking for scalability, speed, cost, as well as the energy and rack-space footprint of big data systems have turned their attention and budgets to GPUs. Although the massively parallel computing power of GPUs significantly speeds up these data-intensive ML and AI workloads, costs can spiral out of control. Join Pepperdata Field Engineer Alex Pierce for a webinar on gaining visibility into cloud GPU resource utilization at the application level and improving the performance of your GPU-accelerated big data applications. Topics include: - Why GPU-accelerated big data applications are going mainstream - Getting visibility into GPU memory usage and waste - Fine-tuning GPU usage through end-user recommendations - Manage costs at a granular level: attributing usage and cost to specific end-users - Monitoring and eliminating waste with GPU monitoring solutions