From inception to insight: Accelerating AI productivity with GPUs (sponsored by Dell Technologies)

Data scientists and machine learning engineers need the flexibility to work in multiple environments without wasting precious time configuring hardware and software and modifying code. Ramesh Radhakrishnan and John Zedlewski walk you through deploying a simple set of technologies for executing end-to-end pipelines entirely on GPUs.

Data scientists and machine learning engineers need the flexibility to work in multiple environments without wasting precious time configuring hardware and software and modifying code. Ramesh Radhakrishnan and John Zedlewski walk you through deploying a simple set of technologies including the Rapid suite of open source software libraries and APIs for executing end-to-end data science and analytics pipelines entirely on GPUs with NVIDIA GPU cloud containers across a combination of laptops, workstations, and enterprise-class Kubernetes clusters without having to modify your code or compromise on performance.