What you should be able to do
Understand the operational pieces needed to ship AI features that are fast, affordable, measurable, and debuggable.
CapstoneDesign an evaluation and monitoring plan for a production AI assistant.
Serve, observe, and scale AI systems
AI Infrastructure
AI infrastructure covers the systems work around models: serving, latency, caching, evals, data pipelines, cost control, and incident response.
User request
Gateway and policy checks
Retrieve, call tools, or generate
Trace, score, and monitor
Answer, fallback, or incident
Concepts to master
- Inference serving
- Batching and caching
- Evaluation pipelines
- Prompt and dataset versioning
- Monitoring, traces, and cost controls
Builder path
How to turn this topic into a working project.
Use this as the bridge from reading to implementation. The goal is to build a small, inspectable version before adding frameworks or production complexity.
- Serve one AI workflow behind an API route with explicit model, prompt, and tool versions.
- Record latency, token usage, retrieval/tool calls, errors, and user-visible outcomes for every request.
- Add caching or batching only after measuring which stage dominates cost or latency.
- Create release checks that compare new model, prompt, data, or tool versions against a frozen evaluation set.
Primary sources
Back to roadmap Open first source