Data Science-focused User Interface
Data scientists benefit from an intuitive user interface enabling Kuberenets-powered on-demand computing power, no prior engineering or DevOps experience required.
Environment Management
Implement software engineering practices, source control management and achieve model reproducibility by allowing data scientists to create, update, and manage compute environment container images. Roll out programming languages, tools and source code libraries once, across the whole organization. Make configuration conflicts, painful and expensive upgrade cycles - across software and hardware - a thing of the past.
Data Access Controls and User Management
Get the granular controls you need in order to provide the right people the right amount of data: Connect data repositories, databases and cloud file systems while controlling access with true user isolation.
Resource Scheduling and Controls
Balance governed access to scalable compute resources, including support for complex, multi-pod, GPU-enabled workloads, and simplify the setup and execution of batch and scheduled jobs
Is Kubernetes the Foundation for Enterprise MLOps?
In this on-demand panel, innovators from NVIDIA and VMware discuss the history of Kubernetes and it's rising prominence in GPU-accelerated data science.
Featuring Craig McLuckie (K8s Co-founder & VP of R&D at VMware), Chris Lamb (VP, CUDA, DGX & Cloud Computing at NVIDIA), and Chris Yang (Domino CTO).
