How to Develop and Deploy AI/ML Workloads at Scale - Prototype to Production in Days, not Months
Explore how organizations can develop and deploy machine learning (ML) workloads at scale on top of Kubernetes in NVIDIA DGX systems, while satisfying the organization’s security and compliance requirements, thus minimizing operational friction and meeting the needs of all the different teams involved in a successful ML effort.
D2iQ Kaptain: https://d2iq.com/products/kaptain
An enterprise-ready distribution of open-source Kubeflow that enables your organization to develop, deploy, and run entire ML workloads in production at scale with consistency and reliability.