The first release of NVIDIA NIM Operator simplified the deployment and lifecycle management of inference pipelines for NVIDIA NIM microservices, reducing the workload for MLOps, LLMOps engineers, and Kubernetes admins. It enabled easy and fast deployment, auto-scaling, and upgrading of NIM on Kubernetes clusters. Learn more about the first release. Our customers and partners have been using…
]]>In the rapidly evolving landscape of AI and data science, the demand for scalable, efficient, and flexible infrastructure has never been higher. Traditional infrastructure can often struggle to meet the demands of modern AI workloads, leading to bottlenecks in development and deployment processes. As organizations strive to deploy AI models and data-intensive applications at scale…
]]>Developers have shown a lot of excitement for NVIDIA NIM microservices, a set of easy-to-use cloud-native microservices that shortens the time-to-market and simplifies the deployment of generative AI models anywhere, across cloud, data centers, cloud, and GPU-accelerated workstations. To meet the demands of diverse use cases, NVIDIA is bringing to market a variety of different AI models…
]]>Modern expectations for agile capabilities and constant innovation—with zero downtime—calls for a change in how software for embedded and edge devices are developed and deployed. Adopting cloud-native paradigms like microservices, containerization, and container orchestration at the edge is the way forward but complexity of deployment, management, and security concerns gets in the way of scaling.
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