Readiness checklists

Input & output schemas are clearly defined

Types, shapes, ranges, optional vs required.

Training is fully reproducible

Code, config, data version, and environment are captured.

Basic metrics & logs are emitted

Latency, error rates, and a core business metric.

There is a safe fallback path

What happens if the model fails or is unavailable?

You have a drift / retraining plan

When will you retrain? On what signals?

Environments are clearly separated

At least dev / staging / prod with clear promotion rules.

CI is mandatory for critical repos

Lint, typecheck and tests run on PRs and block merges.

Centralized logs and metrics exist

Teams know where to look when something breaks.

Access control is in place

Least privilege for data, models, and deployment targets.

Onboarding docs exist and are discoverable

One link to share for "How do I deploy a model here?"

You can describe 1–2 concrete ML systems you worked on

What the system did, where it ran, who used it.

You can quantify impact

Latency, reliability, cost, or business metrics that improved.

You can talk through your tooling choices

CI, infra, orchestrators, registries, monitoring, etc.

You have a failure story ready

What broke, what you learned, and what you changed.

You have a learning path you’re following

Tie it back to the Learning Paths and Roles pages.