Running AI Workloads on Kubernetes

Study guide covering GPU management, training workloads, model serving, MLOps pipelines, networking, and production scaling patterns for AI on Kubernetes.

Textbook

Read the full textbook Long-form companion to the chapter guides →

Chapter Guides

  1. 1 Introduction to AI on Kubernetes
  2. 2 GPU and Accelerator Management
  3. 3 Storage and Data Management for AI Pipelines
  4. 4 Training Workloads: Jobs, Operators, and Frameworks
  5. 5 Model Serving and Inference
  6. 6 Resource Scheduling and Cluster Optimization
  7. 7 MLOps and ML Pipelines on Kubernetes
  8. 8 Networking and Security for AI Workloads
  9. 9 Monitoring, Observability, and Troubleshooting
  10. 10 Production Patterns and Scaling AI Platforms