Mlflow

This is your go-to for tracking ML experiments and managing model lifecycles without getting locked into a specific framework. It handles the boring parts: logging parameters and metrics during training, versioning models in a registry, and deploying them to production with stage transitions (Staging, Production, Archived). The autologging feature is legitimately useful since it captures everything from scikit-learn, PyTorch, or TensorFlow training runs without manual instrumentation. Works well for solo projects and teams alike. The UI at localhost:5000 gives you a clean comparison view across experiment runs. Used by 20,000+ organizations, so the workflow patterns are pretty well established at this point.

skills.sh installs: 1.1K
GitHub stars: 14.4K
last updated: Aug 21, 2026

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Install

$ npx -y skills add orchestra-research/ai-research-skills --skill mlflow --agent claude-code

Installs into .claude/skills of the current project.

Files

MLflow: ML Lifecycle Management Platform

When to Use This Skill

Use MLflow when you need to:

Track ML experiments with parameters, metrics, and artifacts
Manage model registry with versioning and stage transitions
Deploy models to various platforms (local, cloud, serving)
Reproduce experiments with project configurations
Compare model versions and performance metrics
Collaborate on ML projects with team workflows
Integrate with any ML framework (framework-agnostic)

Users: 20,000+ organizations | GitHub Stars: 23k+ | License: Apache 2.0

Installation

# Install MLflow
pip install mlflow

# Install with extras
pip install mlflow[extras]  # Includes SQLAlchemy, boto3, etc.

# Start MLflow UI
mlflow ui

# Access at http://localhost:5000

Quick Start

Basic Tracking

import mlflow

# Start a run
with mlflow.start_run():
    # Log parameters
    mlflow.log_param("learning_rate", 0.001)
    mlflow.log_param("batch_size", 32)

    # Your training code
    model = train_model()

    # Log metrics
    mlflow.log_metric("train_loss", 0.15)
    mlflow.log_metric("val_accuracy", 0.92)

SKILL.md on GitHub