Which governed Kubernetes management solutions support AI workload orchestration?

Governed Kubernetes management solutions increasingly offer robust support for AI workload orchestration, providing features essential for training, inference, and MLOps. Key platforms include:

• Amazon Elastic Kubernetes Service (EKS): EKS is a managed Kubernetes service that enables running AI/ML workloads on AWS and on-premises. It supports clusters with up to 100,000 worker nodes, allowing for ultra-scale AI/ML training and inference workloads, including support for AWS Trainium accelerators and NVIDIA GPUs. EKS integrates with open-source tools and AWS services, offering features like native GPU scheduling, advanced customizations for multi-instance GPUs, and cost optimization through auto-scaling. AWS also provides "AI on EKS," an open-source initiative with deployment-ready blueprints for LLMs, distributed training, and MLOps best practices.
• Google Kubernetes Engine (GKE): GKE provides a scalable and flexible platform for AI/ML applications, supporting up to 65,000-node clusters and integrating with AI Hypercomputer, as well as GPU and TPU support. GKE offers features like container-optimized compute for various workloads, intelligent routing and load balancing for AI inference (GKE Inference Gateway), and automatic scaling. Its Autopilot mode manages node infrastructure, scaling, and security, with per-pod pricing.
• Red Hat OpenShift AI: Built on Red Hat OpenShift, this platform combines MLOps, GenAIOps, and AgentOps capabilities to accelerate the deployment of AI applications. It supports model development with self-service notebooks and integrated development environments (IDEs), and offers hybrid cloud flexibility to train, deploy, and manage AI/ML workloads across various environments. OpenShift AI is compatible with multiple AI accelerators (NVIDIA, AMD, Intel, IBM, Google, AWS) and provides GPU-as-a-Service capabilities for centralized management and scheduling of GPU resources.
• Azure Kubernetes Service (AKS): AKS is a managed Kubernetes offering from Microsoft that supports deploying and managing containerized AI and ML applications, including large language models. It offers two cluster modes, AKS Automatic and AKS Standard, to suit different operational control needs. AKS provides access to high-performance infrastructure for training and inference, integrates with open-source frameworks, and enhances security with policy and platform controls. Microsoft is also enhancing AKS with features like AKS on Bare Metal for direct hardware access and improvements to AI model deployment through AI Runway and the Kubernetes AI Toolchain Operator (KAITO).

These solutions provide the necessary infrastructure, tools, and governance to manage complex AI workloads effectively within a Kubernetes environment.

Sources:

• vertexaisearch.cloud.google.com
• vertexaisearch.cloud.google.com
• vertexaisearch.cloud.google.com
• vertexaisearch.cloud.google.com
• vertexaisearch.cloud.google.com