mlops

Models don't crash, they decay quietly while dashboards stay green. The four-layer monitoring stack, data vs concept drift with PSI numbers, and the label-lag problem that makes naive accuracy monitoring useless.

The most expensive bug class in production ML: two implementations of 'the same' feature that quietly disagree. The diagnostic that finds it in an afternoon, and the architecture that makes it impossible.

Half the value of this question is the second clause. Feature stores solve three specific problems, and FDEs earn trust by naming the team sizes and model counts below which they're expensive overkill.

"It depends" is correct and unhelpful. The decay-curve method that replaces guesswork with measurement, trigger-based vs scheduled retraining, and the safety rails without which auto-retraining is an outage generator.

The capstone panel question that ties the whole track together. The spine, data, training, deployment, monitoring, operating model, plus the maturity-staging judgment that separates architects from slide-readers.

The standard AWS ML Engineer opener. Most candidates recite 'data and model versioning' and stall, the answer interviewers score highest explains why ML tests can't be pass/fail and what continuous training adds as a third loop.

The 45-minute MLOps capstone reported from Capital One and Uber loops. The component walkthrough is table stakes, what's graded is sequencing, the batch-vs-realtime call made early, and knowing which pieces you'd cut for v1.

The 'works on my GPU box, CUDA error 803 in prod' bug, solved at the source. What the host driver actually pins, why the toolkit version is fungible, and the multi-stage build that ships a lean reproducible image.

The dataset is too big to fit on disk, so you stream it. The components that keep the GPU fed, the throughput math that says whether you'll be I/O-bound, and the failure handling that makes a multi-day run resumable.

Logs, traces, and model-quality signals from hundreds of LLM products at firehose volume. The storage split that keeps queries fast, the alerting that survives drift and toxicity at scale, and why sampling has to be cost-aware and tail-biased.

Two hard numbers in the contract: 95% accuracy and $0.50 per resolution. The job is an eval and rollout system that proves both hold on real traffic before you scale, with rollback triggers wired to the same numbers.