mlflow

Files (4)

• SKILL.md
• deployment.md · references/
• model-registry.md · references/
• tracking.md · references/

SKILL.md (first lines)

# 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")

# Start a run
with mlflow.start_run(run_name="baseline-model"):
    # Log params
    mlflow.log_param("model", "ResNet50")
    mlflow.log_param("epochs", 10)

    # Train
    model = train()

    # Log metrics
    mlflow.log_metric("accuracy", 0.95)

    # Log model
    mlflow.pytorch.log_model(model, "model")

# Run ID is automatically generated
print(f"Run ID: {mlflow.active_run().info.run_id}")
```

### 2. Logging Parameters

```python
with mlflow.start_run():
    # Single parameter
    mlflow.log_param("learning_rate", 0.001)

    # Multiple parameters
    mlflow.log_params({
        "batch_size": 32,
        "epochs": 50,
        "optimizer": "Adam",
        "dropout": 0.2
    })

    # Nested parameters (as dict)
    config = {

From Orchestra-Research/AI-Research-SKILLs, MIT.

More from Orchestra-Research/AI-Research-SKILLs