mlflow.evaluate & Model Validation

Evaluation as a first-class step

mlflow.evaluate() runs a logged model against a labelled dataset and automatically logs metrics, diagnostic plots (ROC, confusion matrix, calibration), and explanations — all attached to the run:

result = mlflow.evaluate(
    model='models:/credit-scoring/1',
    data=eval_df,
    targets='defaulted',
    model_type='classifier',
    evaluators='default',
)

Validation thresholds catch bad models before promotion

Pair it with validation_thresholds so a model that fails to meet a bar raises rather than silently shipping:

from mlflow.models import MetricThreshold
mlflow.evaluate(
    ..., 
    validation_thresholds={
        'roc_auc': MetricThreshold(threshold=0.9, greater_is_better=True),
    },
    baseline_model='models:/credit-scoring/Production',
)

Now CI compares the candidate against the live model and fails the build on regression — the quality gate from the last lesson, but with batteries included.

mlflow.evaluate is the automated health check at a clinic: one visit produces blood pressure, bloodwork and an X-ray, and flags anything outside the reference range — instead of you ordering each test by hand and eyeballing the results.