Guided learning path
Production AI Infrastructure
Build reliable AI services through explicit system boundaries, policy, validated outputs, durable execution, evaluation, and workload admission.
Who this path is for
Backend and platform engineers taking AI workflows beyond a prototype.
What you will learn
Place policy and provider boundaries; validate model output before acting; design durable stages and retries; connect evaluation and resource admission to production operation.
Prerequisites
Experience with APIs, databases, background jobs, and distributed-system failure modes. Kubernetes knowledge is useful for the workload-admission article.
Difficulty and workload
Advanced. Read the five-part Production AI Systems sequence in order, then explore admission control as a separate infrastructure topic.
Suggested reading sequence
- A Production AI System Is More Than a Model Call
- The Model Gateway Is a Policy Boundary, Not a Proxy
- From Model Output to Trusted Application Data
- Long-Running AI Work Is a Durable Workflow
- Evaluation Is the Control System for Production AI
- Before the Pod Exists: How Kueue Decides Whether an AI Job May Run
This reading sequence uses existing articles. New guided lessons will appear in the published-lessons list below. Return to all learning paths to explore a related subject.
Curriculum
Published lessons
- No lessons have been published in this path yet.