Section 20 of 24 · 5 lessons

Deploying and Maintaining Models

A model needs to keep behaving as intended after it leaves a training script. Botie will save the full prediction procedure, check incoming records, preserve model versions, and inspect later performance. We will also work through what a useful rollback must restore.

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From Training to a Repeatable Prediction

Saved weights need the preparation and feature meanings that made them useful. Preserve that procedure and validate the records entering it.

  1. 01Saving the Whole Prediction ProcedureWeights alone do not reproduce a prediction. Botie also needs feature order, fitted preparation, and the rules connecting them.
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  2. 02Checking What Arrives at the ModelA model can produce a number from the wrong columns or units. Define and validate the request before trusting its arithmetic.
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Running and Identifying a Model

A prediction should stay consistent across requests and traceable to a particular version. Test batch behaviour and preserve complete artifacts before changing the served model.

  1. 03Keeping a Prediction Consistent Across RequestsThe same input should have the same preparation and result whether it arrives alone or in a batch. Keep prediction free of accidental fitting and mutable shared state.
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  2. 04Knowing Which Model Produced an AnswerA result needs a link to the model, preparation, data, and code that produced it. Version the prediction procedure so comparisons and rollbacks are meaningful.
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Maintaining the Prediction Service

New inputs and later outcomes can reveal different problems. Monitor data, accuracy, and service behaviour separately, with a tested response when something changes.

  1. 05Checking the Model After It Meets New DataInputs, outcomes, and service behaviour can change after launch. Monitor each separately and keep a tested route back to a known compatible version.
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