Lilly Tech Systems
Kubernetes for ML/AI
Learn Kubernetes fundamentals for deploying and managing machine learning and AI workloads. Covers K8s architecture, namespaces for team isolation, resource quotas for GPU management, operators for ML frameworks, and production best practices.
Course Lessons
Follow the lessons in order or jump to any topic.
1. Introduction
Why Kubernetes for ML/AI, the container paradigm, and the K8s ML ecosystem overview.
2. Architecture
Kubernetes architecture for ML: control plane, worker nodes, GPU scheduling, and device plugins.
3. Namespaces
Use namespaces for team isolation, environment separation, and multi-tenant ML clusters.
4. Resource Quotas
Configure resource quotas and limit ranges for GPU, CPU, and memory management across teams.
5. Operators
Use Kubernetes operators for ML frameworks: Training Operator, KServe, Kubeflow, and Ray.
6. Best Practices
Production patterns for ML on Kubernetes: scheduling, security, monitoring, and GitOps.