Developer Blog: Securing and Accelerating End-to-End AI Workflows with the NGC Private Registry

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AI is moving from research to production and enterprises are embracing its power to develop and deploy it across a wide variety of applications, including retail analytics, medical imaging, autonomous driving, and smart manufacturing. 

However, developing and deploying open source AI software inherently has its own challenges. Data scientists need optimized software, developers need the right tools to integrate AI models into their products, DevOps engineers need automation tool sets to deploy production software, and system administrators must provide the right infrastructure. 

NGC provides you with easy access to GPU-optimized containers for deep learning (DL), machine learning (ML), and high performance computing (HPC) applications, along with pretrained models, model scripts, Helm charts, and software development kits (SDKs) that can be deployed at scale. 

As data scientists build custom content, storing, sharing, and versioning of this valuable intellectual property is critical to meet their company’s business needs. To address these needs, NVIDIA has developed the NGC private registry to provide a secure space to store and share custom containers, models, model scripts, and Helm charts within your enterprise.

Before we delve into the salient features of NGC private registry, here’s a bit more on what each of the artifacts mean. 


Containers package software applications, libraries, dependencies, and run time compilers in a self-contained environment so they can be easily deployed across various compute environments. The deep learning frameworks and HPC containers from NGC are GPU-optimized and tested on NVIDIA GPUs for scale and performance. With a one-click operation, you can easily pull, scale, and run containers in your environment. 

Read the full blog, Securing and Accelerating End-to-End AI Workflows with the NGC Private Registry, on the NVIDIA Developer Blog.