deployment

Offline metrics gate releases; online metrics tell you what actually happened with users. The signal is knowing why offline wins routinely fail to hold online, and how the two feed each other.

Accuracy ties are common, and accuracy is rarely the deciding factor anyway. The real tiebreakers are the ones a junior candidate forgets to mention. Here is the checklist a staff engineer runs before shipping either one.

Most CV systems fail at the seams: a preprocessing mismatch between training and serving, not the model. The signal is naming every stage from ingestion to serving and the one consistency invariant that breaks teams. Here is the answer.

This is two questions hiding as one: how would you have caught it before full rollout, and how do you debug it now. Answer both and you signal senior judgment. Here is the staged-rollout and root-cause playbook.

Prompts buried in source code mean every wording tweak is a full deploy, and nobody knows which prompt is live. Treating prompts as managed, versioned configuration is the fix. Here is the platform.

Taking a cloud-API POC into a bank or defense network kills your model provider, your judge API, and your vendor dashboards all at once. Learn the signed-bundle supply chain, local evaluation loop, and zero-egress observability that make an air-gapped LLM stack actually operable.

The trap is describing plain software CI/CD. The signal is the three things ML adds: data validation, a model-quality gate against a baseline, and versioning data plus model plus code together. Here is what is genuinely different.

Deploying a model is not flipping a switch. The signal is knowing what each rollout strategy validates, why shadow is uniquely useful for ML, and keeping rollback one step away. Here is the comparison and when to use each.

LLMOps is not MLOps with bigger models. The signal is the genuinely new surfaces (prompts as deployable artifacts, eval-driven development, often no training step) and treating prompts with the same version discipline as code.

A bad model in production needs a fast, safe rollback, but ML rollback is trickier than code: the model is data and the truth signal lags. The signal is pre-defined criteria plus a previous version kept warm. Here is the answer.

Shadow deployment validates a new model on real traffic without exposing users to it. The signal is knowing outputs are served in parallel then discarded, and how it complements canary and A/B. Here is the answer.

Champion-challenger is how you improve a production model without betting the business on a hunch: the live model versus candidates competing on real metrics. The signal is the ongoing-competition framing and disciplined, pre-committed promotion.

'Which model is in prod right now, and how did it get there?' should be a one-second lookup with an audit trail. If it isn't, you have a rollback and compliance problem already. Here is the promotion pipeline.

A data engineer renames a column and three production models silently start scoring on garbage. Features are a shared contract, and changing them needs the same care as a breaking API change. Here is the discipline.

'Works on my machine' is a dependency and environment problem, and for ML it includes CUDA versions and exact library pins that change numerical results. Here is how to make the production environment match training.

A green build still means nothing for model quality. The signal is an eval harness that runs deterministically in CI, compares against a frozen baseline, and blocks the merge on regressions. Here is how to make it fast, stable, and trusted.

Promotion is the easy part. Governance is who is allowed to ship what, on whose sign-off, with what documented, and how a model gets retired. Here is the control plane an auditor actually asks for.

Manual rollback means minutes of damage while someone wakes up. The signal is defining the trigger signals, thresholds, and guardrails that revert automatically, without flapping on noise. Here is how to make the loop safe.

Saying 'route 5% to the canary' is the easy part. The signal is how you split traffic deterministically, gather enough signal to decide, and ramp on evidence rather than vibes. Here is the mechanics layer.

A platform that no one uses is wasted headcount; a platform built too early is premature. The signal is justifying the investment by leverage and designing a paved path teams adopt willingly. Here is the framing.

A Deployment and a Service will serve a model, but GPUs break every Kubernetes default: scheduling, probes, autoscaling signals, and rollouts. Here is the setup that survives production, and when KServe earns its complexity.

A failed prod deployment is a stress test of judgment under pressure. Interviewers want to see stabilize-first instincts, clean communication, and a root-cause fix that prevents a repeat. Here is the arc.