ML Scoring (ONNX)

ONNX (Open Neural Network Exchange) is a portable format for trained models. The model_score operator runs a registered ONNX model over the batch as a derived column and compares its output with an op and value — letting a model verdict participate in the same rule tree as your deterministic checks.

The model_score leaf

Name the registered model, list the input features in order, and set the threshold:

- model_score:
    model: fraud_logreg
    features: [amount, account_age_days, merchant.risk.score]
    op: gt
    value: 0.7

This leaf matches when the model's score for a record is greater than 0.7. Features may use dotted names like merchant.risk.score for nested input.

Register the model

Register the ONNX artifact with the engine by name, then reference that name from the rule.

   import blazerules

   engine = blazerules.RuleEngine()
   engine.register_model("fraud_logreg", "models/fraud_logreg.onnx")
   engine.load_rules("rules.yaml")

   result = engine.evaluate_ndjson(batch_bytes)

Model files can be loaded from a local path or an s3:// URL, the same as rule files.

register_model(...) can be called before or after load_rules(...). Models are resolved by name when the derived model channel is scored; re-registering a model name swaps the model used by later batches.

One backend, many model types

BlazeRules consumes the .onnx artifact and nothing else. XGBoost, LightGBM, scikit-learn, and neural networks all export to ONNX, so the model author's framework is irrelevant at inference time — export to .onnx and register it.

Derived columns are computed once per batch

model_score is a derived column. It is computed once per batch, and only when a rule actually references it. Rules that don't use the model add no inference cost, and there is no per-record interpreter overhead inside the rule tree.

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ONNX support is a build option

model_score requires the engine to be built with BLAZERULES_ENABLE_ONNX (default ON). When built with it OFF, model_score rules are rejected at compile time and register_model throws. See the error reference and troubleshooting pages if you hit this.

Model scores are internal derived columns, not public input fields. The rule only sees the model_score predicate result. Missing or null numeric features are passed to ONNX as NaN; if a model is not registered, its feature count does not match, or inference fails, that model channel yields zero scores for the batch instead of throwing mid-batch.

Where to go next