# Mlflow

Track ML experiments, manage model registry with versioning, deploy models to...

- Repository: https://github.com/Orchestra-Research/AI-Research-SKILLs (14176 stars)
- Category: Data and ML
- License: MIT
- Last updated: 2026-10-11
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add Orchestra-Research/AI-Research-SKILLs --skill mlflow -g -a claude-code -y`
Codex: `npx skills add Orchestra-Research/AI-Research-SKILLs --skill mlflow -g -a codex -y`
Cursor: `npx skills add Orchestra-Research/AI-Research-SKILLs --skill mlflow -g -a cursor -y`
Gemini CLI: `npx skills add Orchestra-Research/AI-Research-SKILLs --skill mlflow -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add Orchestra-Research/AI-Research-SKILLs`

## Description

Track ML experiments, manage model registry with versioning, deploy models to production, and reproduce experiments with MLflow - framework-agnostic ML lifecycle platform

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# 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

```bash
# 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

```python
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)

    # Log model
    mlflow.sklearn.log_model(model, "model")
```

### Autologging (Automatic Tracking)

```python
import mlflow
from sklearn.ensemble import RandomForestClassifier

# Enable autologging
mlflow.autolog()

# Train (automatically logged)
model = RandomForestClassifier(n_estimators=100, max_depth=5)
model.fit(X_train, y_train)

# Metrics, parameters, and model logged automatically!
```

## Core Concepts

### 1. Experiments and Runs

**Experiment**: Logical container for related runs
**Run**: Single execution of ML code (parameters, metrics, artifacts)

```python
import mlflow

# Create/set experiment
mlflow.set_experiment("my-experiment")

The rest: https://github.com/Orchestra-Research/AI-Research-SKILLs/blob/HEAD/13-mlops/mlflow/SKILL.md