Kubernetes Best Practices for AI Workloads

Kubernetes has become the standard for orchestrating containerized applications, including AI and ML workloads.

GPU Scheduling

Configure NVIDIA device plugins and set resource limits for GPU allocation. Use node affinity to place workloads on GPU-enabled nodes.

Model Serving Patterns

• KServe: Standard model serving with autoscaling
• Triton Inference Server: High-performance multi-model serving
• Seldon Core: Enterprise MLOps platform

Autoscaling Strategies

Implement HPA based on custom metrics like inference latency or queue depth. Consider vertical pod autoscaler for batch processing jobs.

Cost Optimization

Use spot instances for fault-tolerant workloads. Implement cluster autoscaler to right-size your infrastructure.

Monitoring and Observability

Deploy Prometheus and Grafana for metrics. Use Jaeger for distributed tracing across your ML pipeline.