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NVIDIA Raises the Standard of Low Code DevOps with the NVIDIA AI Enterprise 2.1

NVIDIA AI Enterprise 2.1 is now generally available for all enterprise users. Today, the global technology leader NVIDIA announced the most advanced version of its AI-powered data and analytics software for enterprise users. The new AI suite would enable users to fully-optimize their IT and Low Code DevOps processes in a highly scalable AI-based environments. These include applications across bare metal, virtual, container, and Cloud environments.

What is NVIDIA AI Enterprise 2.1?

The latest NVIDIA AI Enterprise 2.1 is part of NVIDIA’s AI enterprise suite. This enterprise-ready AI framework allows DevOps teams to run AI operations from anywhere, anytime on NVIDIA-Certified Systems™ accelerated by GPUs. These also run on certified CPUs and Cloud innovation centers wherever NVIDIA Enterprise Support is available.

NVIDIA’s AI Enterprise 2.1 is an addition to the extensive array of data science, AI and machine learning tools for innovators and open source DevOps teams. These tools include:

  • NVIDIA TAO Toolkits
  • NVIDIA TensorRT
  • NVIDIA MAGNUM IO, and much more.

List of updates on AI Enterprise 2.1

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  • Advanced Data science capabilities on RAPIDS
  • Low Code AI modeling with integration on NVIDIA TAO Toolkit
  • Accessible AI in hybrid and multi cloud environment
  • Support on AI-agnostic platforms such as Red Hat Open Shift, Azure NVads A10 v5 and others
  • Cost-effective GPU sharing to support latest AI machine learning frameworks

Working with the AI Enterprise 2.1 would open new opportunities in the Red Hat OpenShift environment. Red Hat OpenShift is the leading DevOps platform for building Kubernetes or K8 capabilities.

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AI standardization on Red Hat OpenShift would ensure AI developers have seamless opportunity to scale across a hybrid-cloud environment.

NVIDIA LaunchPad tabs for the AI Enterprise 2.1 include:

  • Multi-Node Training for Image Classification on VMware vSphere with Tanzu
  • Deploy a Fraud Detection XGBoost Model using NVIDIA Triton
  • Develop a Custom Object Detection Model with NVIDIA TAO Toolkit and Deploy with NVIDIA DeepStream
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