mlds

When a model auto-approves loans or routes patients, the probability is the product, not a dashboard number. The calibration-plus-abstention design that lets a customer trust an automated decision, and the subgroup trap that gets it pulled in audit.

The most expensive leakage bug in feature-store ML: training joins that grab feature values from after the label event. The timeline that exposes it, the one query that proves it, and the fix that survives audit.

A budget-constrained design problem, not an ideology question. The per-call math that rules an LLM in or out, the cascade that keeps quality while cutting cost 90%, and the SLO arithmetic that decides where the LLM is even allowed to sit.

When treatment leaks between units, a clean A/B lies in both directions. The interference taxonomy, when to reach for switchback vs cluster vs geo designs, and the analysis trap that makes naive standard errors useless.

Not leakage, not skew, not drift: the model learned exactly what you asked, and you asked for the wrong thing. The proxy-label mismatch that passes every test, the audit that catches it, and why this is the failure no metric can see.

Bandits sound strictly better and usually are not. The regret-vs-inference tradeoff, the three conditions that actually favor a bandit, and the production failure that makes adaptive allocation a debugging nightmare.