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hung-phan
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experiment-tracking

Track machine learning experiments and manage model lifecycles with MLflow, Weights & Biases, and Trackio.

1|Updated Jun 4, 2026
Search Tags:#MLflow#experiment tracking#model registry#Trackio#Weights & Biases
One-click install
npx skills add https://github.com/hung-phan/ml-skills --skill experiment-tracking-hung-phan
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: experiment-tracking
Source: https://github.com/hung-phan/ml-skills/tree/main/skills/ml-review/references/ml-training/experiment-tracking
Command: npx skills add https://github.com/hung-phan/ml-skills --skill experiment-tracking-hung-phan

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill addresses the challenge of experiment tracking and model registry in machine learning projects, enabling reproducibility, comparison, and lifecycle management of trained models.

Core Features & Use Cases

  • Experiment Tracking: Record and track all runs, hyperparameters, metrics, and artifacts automatically.
  • Model Registry: Manage the lifecycle of trained models, from staging to production.
  • Use Case: Use this Skill to keep track of various experiments on model training, enabling easy comparison and analysis of results.

Quick Start

Log a new run with MLflow using the following command:

mlflow run <run_directory> -- experiment-name <experiment_name>

Frequently Asked Questions about experiment-tracking

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

FAQPage Schema
How do I track machine learning experiments and manage the lifecycle of trained models?▼

Track machine learning experiments and manage the model lifecycle by logging runs, hyperparameters, and artifacts with MLflow, Weights & Biases, and Trackio to ensure reproducibility and comparison across trained models.

What is the best way to compare model training runs and hyperparameters?▼

Comparing model training runs and hyperparameters is done by automatically recording metrics and artifacts in MLflow or Weights & Biases, enabling straightforward analysis and side-by-side comparison of experiment results.

Can I manage model registry stages from staging to production with MLflow?▼

Yes, MLflow supports managing the model registry lifecycle, allowing you to transition trained models from staging to production while maintaining a centralized repository for artifact and version tracking.

Does this approach work with both Weights & Biases and Trackio for experiment tracking?▼

Yes, experiment tracking works with Weights & Biases and Trackio alongside MLflow, providing integrated dependencies for projects that require comprehensive reproducibility and lifecycle management of trained models.

How do I log a new run in MLflow for an experiment?▼

Log a new MLflow run by executing the command `mlflow run <run_directory> -- experiment-name <experiment_name>` to automatically record hyperparameters, metrics, and artifacts for your tracked experiment.

When do I need experiment tracking for my machine learning projects?▼

You need experiment tracking when projects require strict reproducibility, automatic logging of runs and metrics, and structured lifecycle management of trained models from staging through production deployment.

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