The integration will create a secure, enterprise-ready platform that enables Azure customers worldwide to quickly build, deploy and manage customized applications using the more than 100 NVIDIA AI frameworks and tools that come fully supported in NVIDIA AI Enterprise, the software layer of NVIDIA’s AI platform.
“With the coming wave of generative AI applications, enterprises are seeking secure accelerated tools and services that drive innovation,” said Manuvir Das, vice president of enterprise computing at NVIDIA. “The combination of NVIDIA AI Enterprise software and Azure Machine Learning will help enterprises speed up their AI initiatives with a straight, efficient path from development to production.”
NVIDIA AI Enterprise on Azure Machine Learning will also provide access to the highest-performance NVIDIA accelerated computing resources to speed the training and inference of AI models.
“Microsoft Azure Machine Learning users come to the platform expecting the highest performing, most secure development platform available,” said John Montgomery, corporate vice president of AI platform at Microsoft. “Our integration with NVIDIA AI Enterprise software allows us to meet that expectation, enabling enterprises and developers to easily access everything they need to train and deploy custom, secure large language models.”
With Azure Machine Learning, developers can easily scale applications, from tests to massive deployments, while using Azure Machine Learning data encryption, access control and compliance certifications to meet security and compliance with their organizational policies requirements. NVIDIA AI Enterprise complements Azure Machine Learning with secure, production-ready AI capabilities and includes access to NVIDIA experts and support.
NVIDIA AI Enterprise includes over 100 frameworks, pretrained models and development tools, such as NVIDIA RAPIDS™ for accelerating data science workloads. NVIDIA Metropolis accelerates vision AI model development, and NVIDIA Triton Inference Server™ supports enterprises in standardizing model deployment and execution.
