---
type: concept
x-llmwiki:
  schemaVersion: "0.1"
  contentHash: 37ce6c8b5136f846e5b6ca45fde8a8f1346a2c93056b1ed04d3ef9698a0b1041
  pageDirectory: concepts
  sources:
    - build-dashboards-with-mlflow-metadata-in-system-tables-databricks-on-aws.md
  confidence: 0.9
  provenanceState: extracted
  freshnessStatus: unverified
  aliases:
    - mlflow-aggregate-metric-analysis-across-experiments
    - MAMAAE
  citations:
    - file: build-dashboards-with-mlflow-metadata-in-system-tables-databricks-on-aws.md
title: MLflow Aggregate Metric Analysis Across Experiments
description: Using system tables to compute summary statistics (e.g., average GPU utilization) across all experiments in a workspace within a given time window.
tags:
  - mlflow
  - analytics
  - aggregation
timestamp: "2026-06-18T14:34:28.706Z"
---

# MLflow Aggregate Metric Analysis Across Experiments

**MLflow Aggregate Metric Analysis Across Experiments** refers to the practice of analyzing metrics (such as GPU utilization, memory usage, or model accuracy) across multiple MLflow experiments simultaneously to identify trends, inefficiencies, or anomalies. This approach provides a workspace-wide view of experiment performance that is difficult to obtain through the standard MLflow UI or REST APIs.

## Overview

The standard MLflow UI and REST APIs are designed for examining individual runs or experiments in isolation. Performing aggregate analysis across experiments requires extensive, time-consuming iteration when using these tools alone. By leveraging MLflow metadata stored in system tables, you can build dashboards that analyze metrics across the entire workspace. ^[build-dashboards-with-mlflow-metadata-in-system-tables-databricks-on-aws.md]

## Use Cases

### Monitoring System Metrics Across Experiments

Aggregate metric analysis is particularly useful for monitoring [system metrics](/concepts/mlflow-system-metrics.md) recorded by MLflow across your workspace. These metrics include:

- GPU utilization
- CPU utilization
- Memory usage

By analyzing these metrics across experiments, you can detect inefficient resource utilization. For example, experiments with an average GPU utilization of less than 10% may warrant investigation to optimize resource allocation. ^[build-dashboards-with-mlflow-metadata-in-system-tables-databricks-on-aws.md]

### Identifying Performance Trends

You can input a specific metric name to get summary statistics across all experiments that contain that metric within a given time window. This enables identification of:

- Experiments with consistently low resource utilization
- Experiments with high memory consumption
- Performance drift over time across different model versions

## Implementation with Dashboards

### Using System Tables

The recommended approach is to build dashboards using MLflow metadata in [system tables](/concepts/mlflow-system-tables.md). System tables provide a structured, queryable interface to MLflow metadata that enables SQL-based aggregation across experiments. ^[build-dashboards-with-mlflow-metadata-in-system-tables-databricks-on-aws.md]

### Example Dashboard

Databricks provides an [example dashboard](https://docs.databricks.com/aws/en/assets/files/mlflow_system_tables_dashboard.lvdash-c47a9bd31724e4ee62a78acffc600d0b.json) that includes aggregate metric analysis capabilities. The dashboard contains a tab where you can:

1. Input a metric name for analysis
2. Get summary statistics across all experiments that contain that metric
3. Filter results within a given time window

The dashboard displays visualizations such as average metric values per experiment, enabling quick identification of outliers. ^[build-dashboards-with-mlflow-metadata-in-system-tables-databricks-on-aws.md]

## Related Concepts

- [MLflow Experiments](/concepts/mlflow-experiment.md) — The organizational unit for MLflow runs
- [MLflow System Metrics](/concepts/mlflow-system-metrics.md) — Metrics recorded automatically by MLflow tracking
- Dashboard Visualization — General guidance on creating visualizations from MLflow data
- [GPU Utilization Monitoring](/concepts/gpu-utilization-monitoring-dashboard.md) — Specific application of aggregate metrics for GPU efficiency
- Workspace-Level Analysis — Analysis scope that spans multiple experiments

## Best Practices

- **Define clear time windows** for analysis to capture relevant data without noise from stale experiments.
- **Standardize metric names** across experiments to ensure consistent aggregation.
- **Set utilization thresholds** (e.g., minimum GPU utilization) to automatically flag inefficient experiments.
- **Combine with single-run dashboards** for drill-down analysis when an outlier experiment is identified.

## Sources

- build-dashboards-with-mlflow-metadata-in-system-tables-databricks-on-aws.md

# Citations

1. [build-dashboards-with-mlflow-metadata-in-system-tables-databricks-on-aws.md](/references/build-dashboards-with-mlflow-metadata-in-system-tables-databricks-on-aws-bdd77682.md)
