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domino-experiment-tracking

Track MLflow experiments in Domino Data Lab with auto-logging.

6|3|Updated Feb 19, 2026
Search Tags:#mlops#model training#experiment tracking#mlflow#model registry#domino data lab
One-click install
npx skills add https://github.com/dominodatalab/domino-claude-plugin --skill domino-experiment-tracking-dominodatalab
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: domino-experiment-tracking
Source: https://github.com/dominodatalab/domino-claude-plugin/tree/main/skills/experiment-tracking
Command: npx skills add https://github.com/dominodatalab/domino-claude-plugin --skill domino-experiment-tracking-dominodatalab

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill streamlines the process of tracking machine learning experiments within Domino Data Lab, making it easier to manage, compare, and reproduce model training runs.

Core Features & Use Cases

  • Experiment Management: Set up and manage MLflow experiments for organized tracking.
  • Auto-Logging: Automatically capture parameters, metrics, and models for popular ML frameworks.
  • Manual Logging: Log custom metrics, parameters, and artifacts for fine-grained control.
  • Model Registry: Register, version, and manage models through different stages (Staging, Production).
  • Use Case: When training multiple versions of a classification model, use this Skill to log each run's hyperparameters, accuracy, and saved model artifact, then compare them to find the best performing version and register it for deployment.

Quick Start

Use the domino-experiment-tracking skill to set up a new MLflow experiment named 'customer-churn-modeling' and enable auto-logging for scikit-learn.

Frequently Asked Questions about domino-experiment-tracking

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

FAQPage Schema
How do I track ML experiment metrics and parameters in Domino Data Lab?▼

You can track ML experiment metrics and parameters in Domino Data Lab using the MLflow-based Experiment Manager to log runs, set up experiments, and compare model training outputs.

Can I auto-log training metrics for scikit-learn, TensorFlow, and PyTorch?▼

Yes, auto-logging is supported for scikit-learn, TensorFlow, and PyTorch frameworks, automatically capturing parameters, metrics, and models during machine learning training runs.

What's the best way to compare model runs and register the best version for production?▼

Compare model runs using MLflow's tracking interface to evaluate metrics, then register the best performing version to the Model Registry, managing its stage transition to Production.

How do I log custom metrics and artifacts manually for fine-grained control?▼

Manual logging allows you to record custom metrics, parameters, and artifacts, providing fine-grained control over exactly what gets tracked in your MLflow experiment runs.

Does this Skill manage model versioning across different deployment stages?▼

Yes, the Model Registry functionality registers, versions, and manages models through different lifecycle stages including Staging and Production for organized deployment tracking.

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