Package a model as a component.

This is the same reference used in Technical mode. Expand it whenever you need the exact numbers, assumptions or further explanation.

The weights are only one part of the model

Imagine recording flower measurements in centimetres, subtracting training averages, dividing by training spreads, and then fitting a classifier. Another application sends raw measurements directly into those weights. Every number is finite and the program runs, but it is computing a different function.

Entering midway: a feature is one input measurement. Preprocessing transforms inputs before prediction. An artifact is a saved file used by the application. An input/output contract states what values mean and which forms are accepted. A digest is a short fingerprint computed from file bytes; changing those bytes changes the fingerprint with overwhelming probability.

The lab packages the existing classical-ML studio’s binary logistic classifier. It was fitted using 89 unique training measurement groups from the licensed Iris dataset. It predicts Iris-virginica versus the other two species. These groups identify identical measurements, not proven specimen identities. Packaging adds no new training and makes no new accuracy claim.

Trace a measurement before trusting its prediction

The component names four features in order: sepal length, sepal width, petal length and petal width. Every measurement uses centimetres. A missing value is explicitly null; zero is rejected because this contract requires positive measurements.

For each coordinate, replace a missing value with that coordinate’s stored training mean. Then subtract the stored training mean and divide by its stored training standard deviation, which measures spread. The resulting value describes how far the measurement lies above or below the training mean in units of training spread. It is dimensionless.

For a separate worked example, suppose a length is 6 cm, its training mean is 5 cm and its training spread is 0.5 cm. The standardized value is (6 − 5) / 0.5 = 2. Read this as “two training spreads above the mean”. This example explains the mechanism; the live table uses the actual fitted values.

Multiply each standardized coordinate by its corresponding learned weight, add those contributions and the bias, then apply the logistic transform to produce a probability between zero and one. The table connects every input to that calculation. The deliberately broken comparison skips scaling while keeping the same weights, so its different number is a software defect, not evidence that another model is better.

Do not refit preprocessing from each request. That would make a request’s meaning depend on what happened to arrive with it. If all four inputs are missing, the stored-fill rule produces standardized zeros and a bias-only prediction. A valid numerical result does not mean the application should act on it; an operating policy may require referral instead.

Define the complete interface

Named JSON fields avoid depending on object key order. The internal weight vector still depends on the contract’s feature order. Reversing that order is incompatible even if all four names are present. Likewise, changing a label list can change the meaning of an otherwise unchanged probability.

The request declares its units. Merely changing “mm” to “cm” does not convert the measurements; divide millimetres by ten before declaring centimetres. Shape checks cannot detect every semantic lie: a plausible in-range number with the wrong meaning may pass. Test known examples and validate upstream measurement processes too.

Check integrity and compatibility separately

The bundle contains three JSON texts: contract, preprocessing and model. Each has a SHA-256 digest. The model also names the exact preprocessing and contract digests it requires. The scaler names its required contract.

A byte change without an updated digest fails integrity. A replacement scaler can have a correct digest for its own bytes and still fail compatibility because the model names a different scaler. A weights-only file cannot establish either the input transformation or output meaning. Try each failure and identify the first check that rejects it.

These checks are not authentication. Someone who replaces every file and rewrites every matching digest can create a self-consistent bundle. Obtain packages through a trusted distribution process; use appropriate signatures or trusted expected digests when provenance matters. A fingerprint alone proves neither who produced a model nor whether its behavior is acceptable.

Activate a replacement only after loading succeeds

The loader parses bounded inert JSON, checks versions, exact fields, numerical ranges, digests and dependency references, then activates the component. A failed replacement leaves the preceding successfully loaded component active. The UI displays its digest so a rejected candidate cannot be mistaken for the active model.

This demonstrates transactional loading: prepare and validate the new component before replacing the old one. It is not a production deployment or traffic-routing service. Release gates, rollback decisions, monitoring and incident response need additional evidence and are separate operations lessons.

Serialize and reload the active bundle, then submit the same request. Its probability should match. Download the same artifact and run it through the Python runtime to test another implementation of the contract. The recorded parity suite contains 152 cases, including missing values, compared against the original fitted pipeline within an absolute probability tolerance of one trillionth (0.000000000001). That checks numerical agreement, not generalization or calibration.

Keep execution and data separate

The required browser exercise uses small CPU arithmetic and locally supplied artifacts. Loading a bundle never executes learner text. The downloadable Python runtime uses only the standard library and inert JSON; it does not need the training framework or dataset to make a prediction. Both runtimes reject oversized files and incompatible inputs.

The source manifest preserves Iris attribution, licence and hashes. The parity record identifies training groups and the training recipe. Reproducing the fitted artifact uses the pinned P03 environment and repository script scripts/build-model-component.py. Running inference alone does not retrain it.

Apply this to your path

Guided experiment: export a valid bundle, reload it and explain one integrity failure, one compatibility failure and one request failure using their visible messages.

Implementation: run the Python component, then independently implement its loader or prediction function. Test renamed fields, reordered object keys, wrong units, missing values, zero spread and a valid replacement scaler bound to the wrong model. Keep parsing, validation and inference separate. Passing supplied examples is practice; implementation quality still needs independent review.

Decision brief: define what evidence is needed to accept a component from another team: input meaning, transformation version, provenance, task evaluation, known failure modes and ownership. Explain why a consistent bundle alone is insufficient for deployment approval.

Continue to account for computation to examine resource assumptions, or revisit the pipeline studio to inspect how the model was fitted.