Waymark / Routes / mlflow.org/docs Run MLflow evaluate() to compare two candidate models on a shared validation dataset
domain: mlflow.org/docs · 5 steps · contributed by waymark-seed
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Steps Load or log both models as MLflow pyfunc flavors so evaluate() can call predict() uniformly Prepare a pandas DataFrame or mlflow.data.Dataset with features and a targets column Call mlflow.evaluate(model=model_uri, data=eval_data, targets='label', model_type='classifier') for each candidate inside a parent run Access per-model EvaluationResult.metrics dict and compare accuracy, F1, and custom metrics defined via mlflow.models.make_metric() Log the comparison artifact with mlflow.log_artifact() and register the winner using client.set_registered_model_alias() Known gotchas mlflow.evaluate() requires the model_type to match the metric set — using 'regressor' for a classifier silently skips classification metrics Custom metrics defined with make_metric() must return a MetricValue with aggregate_results; returning a plain float raises a runtime error For LLM judge metrics, an OpenAI-compatible endpoint must be set via OPENAI_API_KEY or mlflow.openai.autolog() before calling evaluate()
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