Page 2 of 8~120 min topic

Model deployment

Define the versioned prediction HTTP service data contract

Types and env contracts make **versioned prediction HTTP service for churn risk scores** fail closed before any provider or cluster call.

~15 min this pageData contract

1Learn the idea

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Define trusted borders

Implement types or schemas around POST /v1/predict so illegal states are unrepresentable at the boundary. For versioned prediction HTTP service for churn risk scores, trusted inputs come from sessions, signatures, pinned digests, or workload identity — not from free-form model text. growth analyst calling /v1/predict with customer features should be able to read the contract and know which fields are optional, which are enumerated, and which abort the request. Constructors and boot paths must not perform provider side effects; injection points keep tests honest.

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Environment and secrets contract

Document required env vars in .env.example with placeholders only. Rotation story belongs later, but the contract already forbids printing secrets and forbids defaulting to fail-open when a dependency is missing. Invariant to encode in types/tests: response includes model_version; unknown feature schema → 422, never silent default. If a config number lacks units, fix the name (timeout_ms, rpm_hard) before writing logic.

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Implementation artifact

class PredictIn(BaseModel):
    customer_id: str
    features: dict[str, float]
class PredictOut(BaseModel):
    score: float = Field(ge=0, le=1)
    model_version: str

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Fixture kit

Create fixtures for the golden path and for MODEL-SCHEMA-DRIFT-22. Name files after the behavior (429-retry-after.json, cross-tenant.json, empty-citation.json) rather than test1. Each fixture carries expected status/code. This kit is the shared language for validation and failure pages.

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Stage depth

Compatibility promise: additive fields may appear only if readers ignore unknowns safely; breaking changes bump a version visible on the wire. For AI payloads, size limits arrive before JSON parse when hostile blobs are a risk. Document how clock skew, idempotency keys, and tracing headers travel through versioned prediction HTTP service for churn risk scores. If you use feature flags later, the contract already states that flags are not authorization. Link each config knob to a unit and a failure mode (“0 means disabled” vs “0 means divide-by-zero”). A peer reviewing the PR should find MODEL-SCHEMA-DRIFT-22 named in a comment on the adversarial fixture.

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Field notes for `model-deployment` / `setup-and-contract`

Generate OpenAPI or a typed client only after the hand schema is stable for one fixture round-trip. Record how errors look on the wire — problem+json, envelope, or bare status — and stick to one. Clock sources must be injectable for skew tests. If webhooks appear later, document signature header names now even as TODOs. Keep sample payloads UTF-8 and free of real emails. Add a makefile or npm script that validates schemas without network. 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.

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Before you start

Why this matters

For Model deployment, sketch the request and response shapes that cross POST /v1/predict without naming a framework. Mark which fields are trusted (session, signatures, digests) versus untrusted (user text, model JSON, webhook bodies). If a field can change authorization, it does not belong in model output. Predict one 422/401 you will assert before coding adapters for versioned prediction HTTP service for churn risk scores.

In the wild

See how this idea shows up as a product and a company — then come back to the lesson. Skills transfer across vendors.

Check your understanding

Page assessment

Answer from memory. Completion is saved from this evidence, not from opening the next page.

1. Can a newcomer list trusted vs untrusted fields?
2. Does boot avoid external side effects?
3. Are fixtures named after MODEL-SCHEMA-DRIFT-22-class failures?

All responses are required.