What is Kubernetes for ML?

Success metrics include system uptime, model performance stability, deployment velocity, and operational cost efficiency.

For teams running 3+ production models with varying resource needs, Kubernetes provides GPU scheduling, auto-scaling, and deployment automation that's difficult to replicate with simpler tools. For a single model with steady traffic, managed services like SageMaker or Vertex AI are simpler. The break-even point is typically when managed service costs exceed $3,000/month or when you need custom deployment patterns. Budget 1-2 months for a team of 2-3 engineers to build an ML-ready Kubernetes platform.

Start with NVIDIA GPU Operator for GPU scheduling, Knative or KServe for model serving with auto-scaling, and Argo Workflows or Kubeflow Pipelines for training orchestration. Add Prometheus and Grafana for monitoring. Use KEDA for event-driven auto-scaling based on queue depth rather than CPU. For experiment management, add MLflow or Weights & Biases. This stack handles most ML platform needs. Avoid installing every ML tool available since each adds operational burden.

Use node pools with GPU-specific instance types rather than mixing GPU and CPU workloads on the same nodes. Implement resource quotas per team to prevent GPU monopolization. Use time-slicing for development workloads that don't need a full GPU. Configure cluster autoscaler to add GPU nodes only when needed and remove them during idle periods. Schedule training jobs during off-peak serving hours to maximize utilization. Track GPU utilization metrics and right-size resource requests quarterly.

For teams running 3+ production models with varying resource needs, Kubernetes provides GPU scheduling, auto-scaling, and deployment automation that's difficult to replicate with simpler tools. For a single model with steady traffic, managed services like SageMaker or Vertex AI are simpler. The break-even point is typically when managed service costs exceed $3,000/month or when you need custom deployment patterns. Budget 1-2 months for a team of 2-3 engineers to build an ML-ready Kubernetes platform.

Start with NVIDIA GPU Operator for GPU scheduling, Knative or KServe for model serving with auto-scaling, and Argo Workflows or Kubeflow Pipelines for training orchestration. Add Prometheus and Grafana for monitoring. Use KEDA for event-driven auto-scaling based on queue depth rather than CPU. For experiment management, add MLflow or Weights & Biases. This stack handles most ML platform needs. Avoid installing every ML tool available since each adds operational burden.

Use node pools with GPU-specific instance types rather than mixing GPU and CPU workloads on the same nodes. Implement resource quotas per team to prevent GPU monopolization. Use time-slicing for development workloads that don't need a full GPU. Configure cluster autoscaler to add GPU nodes only when needed and remove them during idle periods. Schedule training jobs during off-peak serving hours to maximize utilization. Track GPU utilization metrics and right-size resource requests quarterly.

For teams running 3+ production models with varying resource needs, Kubernetes provides GPU scheduling, auto-scaling, and deployment automation that's difficult to replicate with simpler tools. For a single model with steady traffic, managed services like SageMaker or Vertex AI are simpler. The break-even point is typically when managed service costs exceed $3,000/month or when you need custom deployment patterns. Budget 1-2 months for a team of 2-3 engineers to build an ML-ready Kubernetes platform.

Start with NVIDIA GPU Operator for GPU scheduling, Knative or KServe for model serving with auto-scaling, and Argo Workflows or Kubeflow Pipelines for training orchestration. Add Prometheus and Grafana for monitoring. Use KEDA for event-driven auto-scaling based on queue depth rather than CPU. For experiment management, add MLflow or Weights & Biases. This stack handles most ML platform needs. Avoid installing every ML tool available since each adds operational burden.

Use node pools with GPU-specific instance types rather than mixing GPU and CPU workloads on the same nodes. Implement resource quotas per team to prevent GPU monopolization. Use time-slicing for development workloads that don't need a full GPU. Configure cluster autoscaler to add GPU nodes only when needed and remove them during idle periods. Schedule training jobs during off-peak serving hours to maximize utilization. Track GPU utilization metrics and right-size resource requests quarterly.