Kubernetes

Why It Matters

Kubernetes matters for AI systems that need to scale. While Docker containerizes your application, Kubernetes handles running many containers across many machines, automatically scaling up during high traffic, restarting failed containers, and distributing load.

For production AI systems, Kubernetes provides features essential for reliability: health checks detect when your model server becomes unresponsive, rolling deployments let you update models without downtime, and resource limits prevent runaway processes from crashing your cluster.

Kubernetes is particularly valuable for AI workloads because of GPU scheduling. You can specify GPU requirements for your pods, and Kubernetes schedules them on nodes with available GPUs. This enables efficient utilization of expensive GPU resources across your organization.

However, Kubernetes adds significant complexity. For most AI applications starting out, simpler deployment options (single Docker containers, managed platforms like Cloud Run or Lambda) are more appropriate. Adopt Kubernetes when you have specific scaling, multi-service, or GPU orchestration requirements.

Implementation Basics

Core Kubernetes concepts for AI:

• Pods are the smallest deployable units, containing one or more containers
• Deployments manage pod replicas and rolling updates
• Services expose pods to network traffic with load balancing
• ConfigMaps/Secrets store configuration and API keys separately from images
• Persistent Volumes provide storage for model files and data

GPU workloads require the NVIDIA device plugin, which exposes GPUs to Kubernetes. You request GPUs in your pod spec:

resources:
  limits:
    nvidia.com/gpu: 1

Scaling patterns for AI:

• Horizontal Pod Autoscaler adds replicas based on CPU, memory, or custom metrics
• Cluster Autoscaler adds nodes when pods can’t be scheduled
• Knative enables scale-to-zero for sporadic inference workloads

Start with managed Kubernetes (GKE, EKS, AKS) rather than self-managing clusters. Use Helm charts or Kustomize for configuration management. Monitor resource usage closely, since GPU-intensive AI workloads can become expensive quickly if scaling isn’t configured properly.