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Accelerate artificial intelligence (AI) and machine learning (ML) models with the D2iQ Kubernetes Platform and NVIDIA DGX™ Systems

As organizations attempt to develop and run machine learning models, they rapidly run into a series of challenges. Data scientists find that they must set up and interact with a complex hardware and software stack. They also face slow performance as well as failures at scale. Lastly, the accumulation of data tends to also attract additional services and applications, creating data gravity. All of which begins to have a compounding effect, causing complexity and preventing digital transformation from occurring.

D2iQ with NVIDIA DGX Systems Ensures Your Successful Day 2 Operations from Day 1

  • Provides advanced governance, security, and compliance capabilities for AI and ML models with NVIDIA DGX systems.
  • Enables AI and ML prototypes to be moved easily to production environments where they will generate measurable business impact.
  • Delivers AI and ML model processing at scale from any location including the edge.
  • Expert training, services, and support necessary to assist your team in standing up and operating your deployment.

Benefits of D2iQ with NVIDIA DGX Systems

Time to Market

Both hardware and software purpose-built to move AI/ML workloads into production rapidly. Built-in and fully-tested GPU support simplifies the process of exploiting the power of the NVIDIA DGX A100.

Enterprise-Grade Functionality

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.

Security and Multi-Tenancy

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.

Joint Solution Capabilities with NVIDIA

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.

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