---
title: Evaluation runs in MLflow | Databricks on AWS
source: https://docs.databricks.com/aws/en/mlflow3/genai/eval-monitor/concepts/evaluation-runs
ingestedAt: "2026-06-18T08:14:49.624Z"
---

Evaluation runs are MLflow runs that organize and store the results of evaluating your GenAI app. An evaluation run includes the following:

*   **Traces**: One trace for each input in your evaluation dataset.
*   **Feedback**: Quality assessments from scorers attached to each trace.
*   **Metrics**: Aggregate statistics across all evaluated examples.
*   **Metadata**: Information about the evaluation configuration.

## How to create evaluation runs[​](#how-to-create-evaluation-runs "Direct link to How to create evaluation runs")

An evaluation run is automatically created when you call `mlflow.genai.evaluate()`. For more information about `mlflow.genai.evaluate()`, see [the MLflow source code](https://mlflow.org/docs/latest/api_reference/_modules/mlflow/genai/evaluation/base.html#evaluate) and [documentation](https://mlflow.org/docs/latest/api_reference/python_api/mlflow.genai.html#mlflow.genai.evaluate).

Python

    import mlflow# This creates an evaluation runresults = mlflow.genai.evaluate(    data=test_dataset,    predict_fn=my_app,    scorers=[correctness_scorer, safety_scorer],    experiment_name="my_app_evaluations")# Access the run IDprint(f"Evaluation run ID: {results.run_id}")

## Evaluation run structure[​](#evaluation-run-structure "Direct link to Evaluation run structure")

    Evaluation Run├── Run Info│   ├── run_id: unique identifier│   ├── experiment_id: which experiment it belongs to│   ├── start_time: when evaluation began│   └── status: success/failed├── Traces (one per dataset row)│   ├── Trace 1│   │   ├── inputs: {"question": "What is MLflow?"}│   │   ├── outputs: {"response": "MLflow is..."}│   │   └── feedbacks: [correctness: 0.8, relevance: 1.0]│   ├── Trace 2│   └── ...├── Aggregate Metrics│   ├── correctness_mean: 0.85│   ├── relevance_mean: 0.92│   └── safety_pass_rate: 1.0└── Parameters    ├── model_version: "v2.1"    ├── dataset_name: "qa_test_v1"    └── scorers: ["correctness", "relevance", "safety"]
