01Module 8 - Kubernetes for MLA complete guide to running machine learning workloads on Kubernetes, from fundamentals to GPU scheduling, training jobs, model serving, Helm, and multi-tenant clusters.02Kubernetes Fundamentals for ML EngineersThe minimum Kubernetes knowledge every ML engineer needs to be productive - pods, deployments, services, resource requests, GPU allocation, probes, and persistent volumes.03Pods, Deployments, and Services - Deep DiveMaster the three core Kubernetes workload primitives for ML engineers - stateless serving with Deployments, traffic routing with Services, and advanced pod patterns for ML.04GPU Scheduling in KubernetesGPU resource management in Kubernetes - NVIDIA device plugin, MIG, time-slicing, node affinity, GPU quotas per namespace, and DCGM monitoring for ML clusters.05Helm for ML DeploymentsHelm charts for ML applications - chart anatomy, parameterizing ML deployments, environment values files, lifecycle hooks for model validation, and umbrella charts for multi-component stacks.06Training Jobs on KubernetesRunning 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.07Autoscaling ML WorkloadsHorizontal Pod Autoscaler, KEDA event-driven autoscaling for GPU metrics, zero-downtime rolling updates with readiness gates, and autoscaling patterns for production ML serving.08KServe and Kubernetes ML OperatorsCustom 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.
