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domino-experiment-tracking

Skill

Track traditional ML experiments in Domino using the MLflow-based Experiment Manager. Covers experiment setup, auto-logging for sklearn/TensorFlow/PyTorch, manual logging, artifact storage, run comparison, and model registration. Use when training ML models, logging metrics and parameters, comparing

0Monitoring

Install

git clone https://github.com/dominodatalab/domino-claude-plugin.git ~/.claude/skills/domino-experiment-tracking

What is domino-experiment-tracking?

Track traditional ML experiments in Domino using the MLflow-based Experiment Manager. Covers experiment setup, auto-logging for sklearn/TensorFlow/PyTorch, manual logging, artifact storage, run comparison, and model registration. Use when training ML models, logging metrics and parameters, comparing model runs, or registering models.

What this can do

Capabilities declared in this component's own frontmatter — not inferred.

Inherit all session tools

Declares no tool restrictions — inherits every session tool

~84 tokens of context used while enabled, before you invoke anything

Documentation

README · ~1 min read

Domino Experiment Tracking Skill

This skill provides comprehensive knowledge for tracking ML experiments in Domino Data Lab using the built-in MLflow-based Experiment Manager.

Key Concepts

Experiment Manager Overview

Domino's Experiment Manager is built on MLflow and provides:

  • Automatic and manual logging of parameters, metrics, and artifacts
  • Run comparison and visualization
  • Model versioning and registry
  • Integration with Domino projects and jobs

Critical Configuration

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