Mlflow Evaluation
Mlflow Evaluation assesses model performance across different environments, streamlining deployment and ensuring consistent results for robust AI systems.
Install on your platform
We auto-selected Claude Code based on this skill’s supported platforms.
Run in terminal (recommended)
claude mcp add mlflow-evaluation npx -- -y @trustedskills/mlflow-evaluation
Or manually add to ~/.claude/settings.json
{
"mcpServers": {
"mlflow-evaluation": {
"command": "npx",
"args": [
"-y",
"@trustedskills/mlflow-evaluation"
]
}
}
}Requires Claude Code (claude CLI). Run claude --version to verify your install.
About This Skill
What it does
This skill enables AI agents to interact with MLflow for model evaluation. It allows users to retrieve and display evaluation metrics, compare different runs, and gain insights into model performance. The agent can access information about various aspects of the model lifecycle, including parameters, metrics, and artifacts.
When to use it
- Debugging Model Performance: When a machine learning model isn't performing as expected, use this skill to examine evaluation metrics in MLflow and identify potential issues.
- Comparing Experiment Runs: Analyze multiple training runs within MLflow to determine which configuration yielded the best results based on defined metrics.
- Auditing Model Lifecycle: Retrieve details about past model deployments and evaluations for compliance or historical analysis.
- Automated Reporting: Generate reports summarizing model evaluation results directly from MLflow data.
Key capabilities
- Retrieval of evaluation metrics from MLflow runs
- Comparison of different MLflow experiment runs
- Access to model parameters, metrics, and artifacts stored in MLflow
- Displaying evaluation results in a user-friendly format
Example prompts
- "Show me the latest evaluation metrics for my 'fraud_detection' model."
- "Compare the accuracy scores of run 123 and run 456 in the 'churn_prediction' experiment."
- "What were the parameters used in the best performing run for the 'image_classification' project?"
Tips & gotchas
- Requires access to an MLflow tracking server. Ensure the agent has appropriate credentials or connection details.
Tags
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Security Audits
| Gen Agent Trust Hub | Pass |
| Socket | Pass |
| Snyk | Pass |
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Passed automated security scans.