Interactive Diagram Click components to explore
Control Plane
Worker Node
Pod
Service
KUBERNETES CLUSTER
CONTROL PLANE (Master)
API Server
etcd
Scheduler
Controller Manager
SERVICES (Load Balancing)
ml-model-service
Ingress
WORKER NODE 1
kubelet
kube-proxy
Container Runtime
Pods
ml-api-a1b2c
ml-api-d3e4f
train-job-xyz
WORKER NODE 2 (GPU)
kubelet
kube-proxy
GPU Plugin
Pods
ml-api-g5h6i
gpu-inference
ConfigMap
Secret
PersistentVolumeClaim
HPA
Welcome!
Getting Started
Click on any component in the diagram to learn about its role in Kubernetes and how it supports ML workloads.
Key Concepts
- Control Plane: The brain that manages the cluster
- Worker Nodes: Machines that run your containers
- Pods: Smallest deployable units
- Services: Stable networking for pods
ML Context
Tonight you'll learn how to deploy ML models as pods, scale them with HPA, and manage the full ML lifecycle on Kubernetes.