Pipelines broke silently and nobody noticed for days, so the first sign of trouble was usually someone downstream asking why a report looked wrong. I rebuilt them with tests, explicit contracts between producers and consumers, and alerting that fired on the data rather than the job.

Instrumented live predictions for drift and degradation on 7 models; problems surfaced on a dashboard, not in support.

We were flying blind on what the model did in production, with no idea it was wrong until a user complained loudly enough. I added monitoring on the predictions themselves, so drift and degradation showed up on a dashboard instead of in the support queue.

Built an A/B analysis layer; 10 product teams now read results without a data scientist in the loop.

Everyone wanted 'AI in the product' and nobody had said what good would look like, so we were one demo away from shipping something confidently wrong. I set the evaluation first — the metric, the holdout, the bar to clear — so we could tell real progress from a nice-looking demo.

Quantized and batched serving; cost per 1k requests fell 45% at the same quality bar.

Search results were mediocre and clickthrough had been flat for a year, with nobody owning the number end to end. I took relevance from the ranking model through the evaluation set to the online test, and made every change defensible against a holdout instead of a hunch.

Grounded answers in retrieval with citation checks; hallucination rate dropped 50% against the eval set.