Ship and explain the versioned prediction HTTP service

From a fresh clone/directory, run the commands that prove fixture row → score in [0,1] with model_version=churn-xgb-1.4.2. A second person plays growth analyst calling /v1/predict with customer features and follows your script without coaching. The demo includes limitations: what versioned prediction HTTP service for churn risk scores still will not do.

Bundle: contract snippet, passing tests, metric snapshot for schema_reject_rate visible and score_psi ≤ 0.1 vs baseline week, security negative probe, rollback note, owner name. Reference MODEL-SCHEMA-DRIFT-22 as the drill you rehearsed. If any item is missing, the ship gate fails even if the happy path dazzles.

For Model deployment, name the human who gets paged, the review date for thresholds, and the condition that triggers reevaluation. Endpoint POST /v1/predict remains the production surface you operate for versioned prediction HTTP service for churn risk scores — not a slide. Keep MODEL-SCHEMA-DRIFT-22 in the handoff template so the next owner inherits the drill.

After the peer demo, schedule the next threshold review date. File a short changelog that mentions MODEL-SCHEMA-DRIFT-22 and the control that addresses it. Archive the evidence pack where your team already stores launch records. Resist rewriting everything “for real production” in one weekend — operate this slice until the metrics bore you, then widen. Final self-check: if telemetry vanished, would you still know to hold? If yes, you learned the operating posture this lane teaches.

Trim the demo script until every command is necessary. Record a peer signature line: name, date UTC, pass/fail. File known limitations as bullets, not apologies. Link the evidence pack from the README. Schedule the next game day on a calendar, even if it is solo. Archive the branch tag or release digest you actually shipped. In this chapter the product is versioned prediction HTTP service for churn risk scores, the human stakeholder is growth analyst calling /v1/predict with customer features, and the incident id you design against is MODEL-SCHEMA-DRIFT-22. Re-state the oracle in your notes — fixture row → score in [0,1] with model_version=churn-xgb-1.4.2 — and keep the invariant visible: response includes model_version; unknown feature schema → 422, never silent default. Track schema_reject_rate visible and score_psi ≤ 0.1 vs baseline week as the scoreboard. Surface under change control: POST /v1/predict. If you only have forty minutes, finish the fixture for schema drift drops a feature; scores shift 0.3 without version bump before polishing UI. Promotion language stays ternary: promote, hold, or roll back based on evidence, not hope.