Chapter 50: Kubernetes for AI Services

You build the kubernetes-deployment skill first, then refine it through core and optional lessons. The FastAPI agent from Part 6 is the running example you deploy and harden on Kubernetes.

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Goals

• Understand Kubernetes architecture (control plane, nodes, declarative model)
• Deploy and operate workloads: Pods, Deployments, Services, Jobs, CronJobs
• Configure apps with ConfigMaps/Secrets and namespace isolation
• Manage resources and scaling: requests/limits, HPA, rolling updates
• Secure clusters with RBAC, health probes, and best practices
• Use kubectl-ai to generate/evaluate manifests
• Produce a reusable Kubernetes deployment skill

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Lesson Progression

#	Lesson	Focus
0	Build Your Kubernetes Skill	Scaffold from docs
1-5	Fundamentals	Architecture, Pods, Deployments, Services
6-12	Production Essentials	Namespaces, config, resources, HPA, RBAC, probes, batch
13	AI Collaboration	kubectl-ai for manifests and ops
14	Capstone: Deploy Agent	Production deployment of the Part 6 API
15	Test and Refine Skill	Validate cross-application transferability
16-22 (Optional)	Advanced Patterns	Init/sidecars, ingress, discovery, stateful workloads, storage, deep security

Each lesson ends with a reflection to test, find gaps, and improve the skill.

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Outcome & Method

You finish with your Part 6 FastAPI agent running on Kubernetes—secured, health-checked, and autoscaled—plus a Kubernetes deployment skill. The 4-Layer progression moves from fundamentals to AI-assisted manifests to a spec-driven capstone, with optional deep dives for advanced scenarios.

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Prerequisites

• Chapter 49 container image pushed to a registry
• Docker Desktop with Kubernetes enabled (or equivalent local cluster)
• Terminal familiarity; Kubernetes experience not required (Lesson 1 covers basics)