9 docs tagged with "kubernetes"

Horizontal Pod Autoscaler, KEDA event-driven autoscaling for GPU metrics, zero-downtime rolling updates with readiness gates, and autoscaling patterns for production ML serving.

GPU resource management in Kubernetes - NVIDIA device plugin, MIG, time-slicing, node affinity, GPU quotas per namespace, and DCGM monitoring for ML clusters.

Liveness vs readiness probes, dependency health checks, health check libraries, SLOs, and building production-grade health endpoints in Python.

Helm charts for ML applications - chart anatomy, parameterizing ML deployments, environment values files, lifecycle hooks for model validation, and umbrella charts for multi-component stacks.

Custom Kubernetes operators for ML workflows - what operators enable, KServe for standardized model serving, Seldon Core, the Kubeflow Training Operator, Argo Workflows, and when to build vs. use existing operators.

The minimum Kubernetes knowledge every ML engineer needs to be productive - pods, deployments, services, resource requests, GPU allocation, probes, and persistent volumes.

A complete guide to running machine learning workloads on Kubernetes, from fundamentals to GPU scheduling, training jobs, model serving, Helm, and multi-tenant clusters.

Master the three core Kubernetes workload primitives for ML engineers - stateless serving with Deployments, traffic routing with Services, and advanced pod patterns for ML.

Running ML training on Kubernetes - Jobs, CronJobs, PyTorchJob and TFJob with the Training Operator, fault tolerance, checkpoint-based recovery, spot node handling, and distributed training patterns.