Container Orchestration for Machine Learning
Explore Kubernetes concepts through hands-on simulations and visual guides
Interactive diagram of Kubernetes cluster components. Click on any component to learn its role in ML workloads.
Watch Horizontal Pod Autoscaler in action. Adjust traffic and see pods scale up and down in real-time.
Understand pod states through animated scenarios. See what happens during crashes, OOM kills, and probe failures.
Searchable command reference with ML-specific tips. Copy commands instantly and learn when to use each one.
By the end of this session, you'll be able to:
Understand clusters, nodes, pods, and services for ML workloads
Create deployments and services for scalable model serving
Configure HPA to scale based on CPU/memory usage
Set up persistent volumes for models and datasets
Use Jobs and CronJobs for training and batch processing
Use kubectl and logging for ML system observability
Manage secrets and configure resource limits
Debug common ML deployment problems with kubectl