Platform engineering for MLOps: Automating AI infrastructure with k0rdent
MLOps is more than deploying AI models: it’s about building scalable, repeatable AI platforms. This session demonstrates how k0rdent automates GPU cluster provisioning, model deployment, and monitoring to streamline cloud-agnostic MLOps workflows
MLOps is not just about deploying AI models but about constructing a scalable, automated AI platform. Setting up GPU-powered clusters, serving models, and monitoring AI workloads often involves tedious manual configurations, slow iteration cycles, and fragmented tools.
This session focuses on scaling AI infrastructure, showcasing how k0rdent automates the entire MLOps lifecycle - from GPU cluster provisioning to model deployment, scaling, and monitoring. Attendees will witness a live demo, where a GPU-enabled Kubernetes cluster is spun up across clouds and regions, deploying AI infrastructure seamlessly.
MLOps can be complex, but with k0rdent, it becomes effortless, enabling fast, repeatable workflows that streamline the entire process.
This session focuses on scaling AI infrastructure, showcasing how k0rdent automates the entire MLOps lifecycle - from GPU cluster provisioning to model deployment, scaling, and monitoring. Attendees will witness a live demo, where a GPU-enabled Kubernetes cluster is spun up across clouds and regions, deploying AI infrastructure seamlessly.
MLOps can be complex, but with k0rdent, it becomes effortless, enabling fast, repeatable workflows that streamline the entire process.
Platform engineering for MLOps: Automating AI infrastructure with k0rdent
MLOps is more than deploying AI models: it’s about building scalable, repeatable AI platforms. This session demonstrates how k0rdent automates GPU cluster provisioning, model deployment, and monitoring to streamline cloud-agnostic MLOps workflows
Presented by
Panelist
Panelist
Panelist
Moderator
Bharath Nallapeta
Senior Software Engineer, Mirantis
MLOps is not just about deploying AI models but about constructing a scalable, automated AI platform. Setting up GPU-powered clusters, serving models, and monitoring AI workloads often involves tedious manual configurations, slow iteration cycles, and fragmented tools.
This session focuses on scaling AI infrastructure, showcasing how k0rdent automates the entire MLOps lifecycle - from GPU cluster provisioning to model deployment, scaling, and monitoring. Attendees will witness a live demo, where a GPU-enabled Kubernetes cluster is spun up across clouds and regions, deploying AI infrastructure seamlessly.
MLOps can be complex, but with k0rdent, it becomes effortless, enabling fast, repeatable workflows that streamline the entire process.
This session focuses on scaling AI infrastructure, showcasing how k0rdent automates the entire MLOps lifecycle - from GPU cluster provisioning to model deployment, scaling, and monitoring. Attendees will witness a live demo, where a GPU-enabled Kubernetes cluster is spun up across clouds and regions, deploying AI infrastructure seamlessly.
MLOps can be complex, but with k0rdent, it becomes effortless, enabling fast, repeatable workflows that streamline the entire process.
Platform engineering for MLOps: Automating AI infrastructure with k0rdent
MLOps is more than deploying AI models: it’s about building scalable, repeatable AI platforms. This session demonstrates how k0rdent automates GPU cluster provisioning, model deployment, and monitoring to streamline cloud-agnostic MLOps workflows
Presented by
MLOps is not just about deploying AI models but about constructing a scalable, automated AI platform. Setting up GPU-powered clusters, serving models, and monitoring AI workloads often involves tedious manual configurations, slow iteration cycles, and fragmented tools.
This session focuses on scaling AI infrastructure, showcasing how k0rdent automates the entire MLOps lifecycle - from GPU cluster provisioning to model deployment, scaling, and monitoring. Attendees will witness a live demo, where a GPU-enabled Kubernetes cluster is spun up across clouds and regions, deploying AI infrastructure seamlessly.
MLOps can be complex, but with k0rdent, it becomes effortless, enabling fast, repeatable workflows that streamline the entire process.
This session focuses on scaling AI infrastructure, showcasing how k0rdent automates the entire MLOps lifecycle - from GPU cluster provisioning to model deployment, scaling, and monitoring. Attendees will witness a live demo, where a GPU-enabled Kubernetes cluster is spun up across clouds and regions, deploying AI infrastructure seamlessly.
MLOps can be complex, but with k0rdent, it becomes effortless, enabling fast, repeatable workflows that streamline the entire process.
Platform engineering for MLOps: Automating AI infrastructure with k0rdent
MLOps is more than deploying AI models: it’s about building scalable, repeatable AI platforms. This session demonstrates how k0rdent automates GPU cluster provisioning, model deployment, and monitoring to streamline cloud-agnostic MLOps workflows
Presented by
Panelist
Panelist
Panelist
Host
Bharath Nallapeta
Senior Software Engineer, Mirantis
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