System Design Studio
Build an AI pipeline, then watch traffic find its breaking point.
Production failures are normally discovered after the fact in an incident channel; the simulator finds and names the exact breaking node live, on a diagram the learner built themselves, before any real traffic exists.
What goes in, what comes out
Learner drags nodes onto a canvas to assemble a real AI system — ingestion, embedding, vector store, retrieval, rerank, generation, cache, guardrail, observability — wiring each to the next as a real request path, not a slide. A simulated traffic ramp (steady, spike, or adversarial burst) then runs against the assembled architecture, and the first node to fail lights up red with its exact failure mode named (cache stampede, quota 429, retrieval timeout, unbounded cost), before the learner ever ships it.
A target use case (e.g., a support RAG bot at 500 req/min), the assembled architecture graph, and a chosen traffic pattern.
A running architecture diagram, a live traffic simulation with a red first-failure node and named failure mode, and an exportable capacity/cost report the learner can defend.
Node set and wiring, per-node capacity/cache settings, traffic pattern (steady/spike/adversarial) and volume, and a cost/latency ceiling the design must survive.
In-module lab for AIE-403 (elective, Architect badge) and a formal input into the AIE-490 capstone defense.
Go deeper, elsewhere
Hand-picked public explainers and open tools that complement this one — always optional, never required, never graded.
Concepts click when you open the machinery.
Three labs are already live and free — the same hands-on style this playground brings to its module.