Module 7: Kubernetes for ML
๐ฏ Learning Objectivesโ
- Understand Kubernetes fundamentals (Pods, Deployments, Services)
- Deploy ML workloads with Kubeflow
- Scale ML applications with autoscaling and GPU scheduling
๐ Module Contentโ
Chapter 1: Kubernetes Basicsโ
Pods, deployments, services, configmaps, secrets, resource management.
Chapter 2: Kubeflowโ
Kubeflow components, pipelines, Katib for hyperparameter tuning, KFServing.
Chapter 3: Scaling ML Workloadsโ
Horizontal pod autoscaling, GPU scheduling, node pools, spot/preemptible nodes.
๐ Let's Begin!โ
Start with Chapter 1: Kubernetes Basics.