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LaurentPRAT-DB
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querying-mlflow-metrics

Aggregates and charts MLflow usage metrics for specified experiments.

Updated Feb 27, 2026
Search Tags:#performance#monitoring#llm#data science#metrics#mlflow
One-click install
npx skills add https://github.com/LaurentPRAT-DB/LPT_claude_config --skill querying-mlflow-metrics-laurentprat-db
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: querying-mlflow-metrics
Source: https://github.com/LaurentPRAT-DB/LPT_claude_config/tree/main/skills/querying-mlflow-metrics
Command: npx skills add https://github.com/LaurentPRAT-DB/LPT_claude_config --skill querying-mlflow-metrics-laurentprat-db

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires mlflow, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill streamlines the process of retrieving and analyzing key performance metrics from MLflow tracking servers, making it easier to monitor model performance and resource utilization.

Core Features & Use Cases

  • Fetch Aggregated Metrics: Retrieve token usage, latency, trace counts, and quality evaluations.
  • Analyze Trends: Visualize token usage over time, latency percentiles, and error rates.
  • Use Case: A data scientist needs to understand the token consumption and latency of their deployed models over the last week. They can use this Skill to quickly generate a summary report directly from their MLflow server.

Quick Start

Run the python script to fetch the total tokens and average latency for experiment ID 1 from your MLflow server at http://localhost:5000.

Frequently Asked Questions about querying-mlflow-metrics

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I retrieve token usage and latency metrics from an MLflow tracking server?▼

To retrieve token usage and latency metrics from an MLflow tracking server, use Python scripts to query the MLflow API for aggregated trace metrics, specifying your target experiment IDs to generate a summary report.

Can I group MLflow trace metrics by time buckets and dimensions?▼

Yes, you can group MLflow trace metrics by utilizing the script's built-in options for time-based bucketing and dimensional grouping, allowing you to analyze trends and usage statistics across specified experiment IDs.

What are the best ways to monitor LLM performance trends using MLflow?▼

The best way to monitor LLM performance trends using MLflow is to fetch and aggregate trace metrics like token consumption, latency percentiles, and quality evaluations to visualize data over time and track cost monitoring.

Does querying MLflow metrics work for cost monitoring of deployed models?▼

Yes, querying MLflow metrics works for cost monitoring of deployed models by fetching and aggregating trace metrics such as token usage and latency, which helps data scientists understand resource utilization over specific periods.

Do I need Python to analyze quality evaluations from MLflow traces?▼

Yes, you need Python to analyze quality evaluations from MLflow traces because the Skill utilizes Python scripts to query the MLflow API and fetch assessment values alongside other trace metrics like trace count and latency.

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