Red Hat introduced Red Hat Enterprise Linux 9, the Linux operating system designed to drive more consistent innovation across the open hybrid cloud, from bare metal servers to cloud providers and the farthest edge of enterprise networks.
D2iQ joined the NVIDIA DGX-Ready Software program, empowering NVIDIA customers to quickly deploy artificial intelligence (AI) and machine learning (ML) models in Day 2 production environments with the D2iQ Kubernetes Platform (DKP).
The NVIDIA DGX-Ready Software program features enterprise-grade MLOps solutions that accelerate AI workflows and improve deployment, accessibility, and utilization of AI infrastructure. With DKP, NVIDIA customers can now simplify model deployment to speed up production processes on their NVIDIA DGX ™ systems, narrowing the time from prototype to production to hours instead of months, all while hiding the complexities of Kubernetes. In addition, DKP ensures data scientists are presented with only relevant information and tools, including training, tuning, and deploying models with Python in notebooks.
"Successful, production-ready AI and ML deployments require both a scalable infrastructure, such as NVIDIA DGX systems, and powerful software like the D2iQ Kubernetes Platform," said Tobi Knaup, CEO of D2iQ. "This collaboration provides enterprises across the globe with a comprehensive solution for moving AI and ML prototypes forward to production environments where they will generate measurable business impact."
As a NVIDIA DGX-Ready Software program certified solution provider, D2iQ provides an end-to-end Kubernetes based AI/ML platform that enables customers to build applications without needing to focus on infrastructure, scalability or security. D2iQ enables organizations to develop and deploy machine learning workloads at scale, while satisfying the organization's security and compliance requirements, minimizing operational friction and meeting the needs of all the teams involved in a successful ML strategy.
"AI workloads require enterprise-grade infrastructure that enables businesses to improve deployment and efficiently accelerate AI and ML workflows," said John Barco, senior director of product management at NVIDIA. "The NVIDIA DGX-Ready program certification ensures that the D2iQ Kubernetes Platform provides advanced governance, security, and compliance capabilities with NVIDIA DGX systems to deliver enterprise AI infrastructure at scale."
Key benefits from the combination of DKP and NVIDIA DGX systems include:
- Faster time-to-market with both hardware and software purpose-built to move AI/ML workloads into production more quickly
- Built-in and fully-tested GPU support simplifies the process of exploiting the power of the NVIDIA DGX A100
- Enterprise-grade and ready for Day 2 Operations with integrated observability, end-to-end security, and cost management
- Centralized observability provides enhanced visibility and control at the enterprise level, with comprehensive logging and monitoring across all clusters
- Enterprise security and multi-tenancy enable ML teams to access shared compute resources (GPUs) in their own isolated workspace, provide end-to-end security with SSO and multi-tenancy, and scale environments, add more users, or infrastructure resources as deployments grow