# ML Tools Integration Guide

Data scientists can now use their preferred ML tools securely!

## Available Tools

- **MLflow** - Experiment tracking and model registry
- **Weights & Biases** - Experiment tracking and visualization  
- **Streamlit** - Interactive web apps
- **Dash** - Plotly-based web dashboards
- **Panel** - Data apps and dashboards
- **Bokeh** - Interactive visualizations

## Quick Start

### 1. Test Tools Available
```bash
cd podman
python test_ml_tools.py
```

### 2. Use MLflow
```python
import mlflow

# Start tracking
with mlflow.start_run():
    mlflow.log_param("epochs", 10)
    mlflow.log_metric("accuracy", 0.95)
```

### 3. Launch Streamlit App
```bash
# In your secure container
streamlit run my_app.py --server.port 8501
```

### 4. Use Dash Dashboard
```python
import dash
import dash_core_components as dcc
import dash_html_components as html

app = dash.Dash(__name__)
app.run_server(debug=True, host='0.0.0.0', port=8050)
```

## Security Features

- Network access limited to localhost only
- Tools pre-approved in security policy
- Container isolation maintained
- No external internet access

## Custom Requirements

Add your own tools via `requirements.txt`:
```
mlflow==2.7.0
wandb==0.16.0
streamlit==1.28.0
```

## Access URLs

- Streamlit: http://localhost:8501
- Dash: http://localhost:8050
- MLflow UI: http://localhost:5000
