Phase 17 · MLOps & Model Deployment
TopicsKubernetes for ML (Overview)
Part of the AI Engineer Roadmap.
Summary
Orchestrating containerized model-serving workloads at scale — handles autoscaling, load balancing and self-healing for production ML services.
How to Learn This
- 1Read Kubernetes core concepts: pods, deployments, services, autoscaling.
- 2Deploy a containerized model-serving app to a local Kubernetes cluster (minikube/kind).
- 3Understand when a project actually needs Kubernetes vs. simpler managed hosting.
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