Validated Designs for AI Solution Brief
■Accelerate ML/DL workloads using Kubeflow and PowerEdge servers
▲Artificial intelligence (AI) and its subsets, machine learning (ML) and deep learning (DL), are gaining widespread adoption for use cases such as computer vision, speech recognition and natural language processing (NLP). Integrating production‑grade AI technologies in well‑defined platforms within the protection of the data center can facilitate wider adoption of advanced computing, extending investments by supporting AI use cases as well as augmenting the resources available to data science teams.
▲But not every IT organization has the time and resources to research, integrate and test all the components required to deploy a customized system to run AI workloads. Building a production‑ready AI system involves combining various components, often from different vendors, and integrating and managing these disparate pieces. As well, deployments are often tied to a specific cluster, so that moving models — for example, between laptops and cloud clusters or from DevOps to production and back — significantly increases complexity and the chance for human errors.
▲Kubeflow together with the Red Hat® OpenShift® Container Platform help address these challenges. Kubeflow is an open‑source Kubernetes®‑native platform designed to accelerate ML workloads. It’s a composable, scalable, portable stack that includes components and automation features to integrate ML tools, so they work together to create a cohesive pipeline that makes it easy to deploy ML applications at scale. Kubeflow requires a Kubernetes environment, such as the Red Hat OpenShift Container Platform, a secure enterprise implementation of open‑source Kubernetes.
▲Running Kubeflow on OpenShift offers several advantages in an ML/DL context. Using Kubernetes as the underlying platform makes models portable, so ML/DL engineers can develop models locally, using a development system such as a laptop, and easily deploy the application to a production Kubernetes environment. In addition, the ability to run ML/DL workloads in the same environment as the rest of your enterprise applications increases control and reduces complexity for IT teams.
▲Together, Dell Technologies and Red Hat take the guesswork and risk out of AI platform deployment and operations. The Dell Technologies engineering‑validated design for AI — OpenShift Container Platform delivers tested, validated, and documented design guidance to help you rapidly deploy Kubeflow and OpenShift on Dell EMC infrastructure.
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[ Artificial intelligence ][ AI ] |
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Please see the document for details |
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2021/11/16 |
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