The Future Is Smart: Cloud Native + AI

Kubernetes a Perfect Match for AI

  • Kubernetes can easily scale to meet the resource needs of AI/ML training and production workloads as well as the continuous development nature of AI/ML models.
  • Kubernetes enables sharing of expensive and limited resources like graphic processing units (GPUs) between developers to speed up development and lower costs.
  • Kubernetes provides a layer of abstraction that enables data scientists to access the services they require without worrying about the details of the underlying infrastructure.
  • Kubernetes provides high availability and failover protection to improve service-level agreements (SLAs) and resiliency.
  • Kubernetes gives organizations the agility to deploy and manage AI/ML operations across public clouds, private clouds, on-premise, and secure air-gap locations, and to easily change and migrate deployments without incurring excess cost. In many use cases, training is done on the cloud, and inference at the edge. Kubernetes provides a single way to manage the many components of an AI/ML pipeline across disparate infrastructures.
  • A smart cloud-native business application consists of a number of components, including microservices, data services, and AI/ML pipelines. Kubernetes provides a single consistent platform on which to run all workloads, rather than in silos, which simplifies deployment and management and minimizes cost.
  • As an open-source cloud-native platform, Kubernetes enables organizations to apply cloud-native best practices and take advantage of continuous open-source innovation. Many of the modern AI/ML technologies are open source as well, and come with native Kubernetes integration.

Smart Cloud-Native Challenges

  • First, because those workloads are so mission-critical, it puts a much higher burden on operations teams to keep those workloads running 24/7 while making sure they are resilient, can scale, and are secure.
  • Second, those workloads tend to include more sophisticated technologies like data workloads, AI workloads, and machine learning workloads, which have their own operational challenges.
  • Third, modern cloud-native applications tend to run on a broad range of infrastructures, from a cloud provider or multiple cloud providers to data centers and edge deployments.

The Answer Is Automation

Taming Kubernetes Complexity

A Firm and Future-Proof Foundation

Keeping Pace with Innovation




The Leading Independent Kubernetes Platform

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The Leading Independent Kubernetes Platform

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