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Building Prediction Interfaces Print

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Serving requests.

WHAT THE INTERFACE MUST DO

Accept input Validate it Preprocess it Predict Postprocess Return a response

WHAT TO VALIDATE

Presence of required fields Types and ranges Category values

WHY STRICTLY

Invalid input produces a confident wrong answer, not an error.

WHAT TO RETURN

The prediction A confidence or probability, where meaningful The model version used

WHY THE VERSION

It is essential for investigating a disputed prediction later.

WHAT TO LOG

Input features, output, model version, latency, and a request identifier.

WHAT TO BE CAREFUL WITH IN LOGGING

Personal data, which must be handled accordingly.

WHAT TO CONFIGURE

Timeouts A maximum request size Rate limiting

WHAT BATCHING PROVIDES

Grouping concurrent requests for efficient inference.

WHAT IT COSTS

Slight latency, traded for throughput.

WHAT TO HANDLE

Model loading at startup, not per request Failure to load, clearly Graceful degradation when a feature source is unavailable

WHAT TO NEVER DO

Return a prediction based on silently substituted inputs.


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