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MLflow includes built-in support for Phoenix evaluators through its third-party scorer interface. This allows Phoenix users to run their existing evaluation metrics within MLflow’s mlflow.genai.evaluate() pipeline alongside experiment tracking and model management.

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Using Phoenix Evaluators in MLflow

Phoenix evaluators such as Hallucination, QACorrectness, and Toxicity can be used directly as MLflow scorers: 
from mlflow.genai.scorers.phoenix import Hallucination, QACorrectness

import mlflow

results = mlflow.genai.evaluate(
    data=eval_dataset,
    scorers=[
        Hallucination(model="openai:/gpt-4o"),
        QACorrectness(model="openai:/gpt-4o"),
    ],
)
For details on available scorers and configuration, see the MLflow Phoenix integration docs.