---
title: "Aurora AI Framework - Complete User Guide | Getting Started Tutorial"
description: "Complete user guide for Aurora AI Framework v1.0.0 - Step-by-step tutorials, installation guide, configuration, and usage examples for enterprise AI platform."
keywords: "Aurora AI user guide, AI framework tutorial, enterprise AI getting started, machine learning guide, AI installation, AI configuration, enterprise AI platform"
author: "Aurora Development Team"
robots: "index, follow"
canonical: "https://aurora-ai.github.io/docs/USER_GUIDE.md"
---

# Aurora AI Framework - Complete User Guide

## Getting Started

### **🚀 Current System Status: LIVE**
- **Web Interface**: http://localhost:8081 - **ACTIVE**
- **Server**: Aurora AI Sci-Fi Interface - **RUNNING**
- **Debug Mode**: Enabled (PIN: 343-268-059)
- **API Health**: All endpoints responding
- **Last Updated**: 2026-05-06

> **📚 Related Documentation**: For complete system architecture, see our [Architecture Guide](ARCHITECTURE.md). For API reference, check our [API Documentation](API_REFERENCE.md).

> **🚀 Installation**: Complete installation instructions available in our [Installation Guide](INSTALLATION.md).

> **🔧 Configuration**: Detailed configuration options in our [Configuration Guide](CONFIGURATION_GUIDE.md).

> **🌐 Interface Access**: The Aurora AI Framework interface is currently running and accessible at http://localhost:8081

### Installation

1. **Clone or download the Aurora framework**
2. **Install dependencies**:
   ```bash
   pip install -r requirements.txt
   ```

3. **Verify installation**:
   ```bash
   python examples/example_usage.py --mode quick
   ```

> **💡 Tip**: For detailed installation instructions, including system requirements and troubleshooting, see our [Installation Guide](INSTALLATION.md).

### Quick Start

1. **Prepare your data** (CSV format) - See [Data Validation Guide](DATA_VALIDATION_GUIDE.md) for data preparation
2. **Configure the framework** in `config/config.yaml` - See [Configuration Guide](CONFIGURATION_GUIDE.md) for detailed options
3. **Run the framework**:
   ```bash
   python main.py
   ```

> **🔍 Monitoring**: After starting, monitor your system with our [Monitoring Guide](MONITORING_ANALYTICS_GUIDE.md).

## Configuration

### Main Configuration File (`config/config.yaml`)

```yaml
app:
  name: Aurora AI Framework
  version: 1.0.0
  description: "Configuration file for the Aurora AI framework."

data_pipeline:
  data_path: "data/input.csv"
  source: "local"
  format: "csv"
  input_file: "data/input.csv"
  output_file: "data/output.csv"
  preprocessing: "standard"

model:
  architecture: "ensemble_model"
  type: classification
  algorithm: "RandomForest"
  parameters:
    learning_rate: 0.01
    num_epochs: 100
    batch_size: 32
  n_estimators: 100
  max_depth: 10
  random_state: 42
  epochs: 10
  batch_size: 32
  optimizer: "adam"

api_server:
  host: 0.0.0.0
  port: 8080
  debug: false

monitoring:
  log_interval: 5
  drift_detection: true
  alerting: true
  alert_threshold: 0.8

security:
  enable_authentication: false
  encryption_key: "L_8Hfm33ainlgyoN0t_3YsGjw-ujM15X8_VsrKrKr5U="
  api_keys:
    internal: "internal_api_key"
    external: "external_api_key"

modules:
  enabled:
    - monitoring
    - alerting
    - data_validation
    - error_tracker
  disabled:
    - emotional_core
    - eternal_art

metadata:
  author: "Aurora Development Team"
  last_updated: "2025-05-06"
```
  drift_detection: true
  alerting: true
  alert_threshold: 0.8

api_server:
  host: "0.0.0.0"
  port: 8080
```

### Data Pipeline Configuration

| Parameter | Description | Default | Options |
|-----------|-------------|---------|---------|
| `data_path` | Path to your data file | Required | Valid file path |
| `format` | Data file format | "csv" | "csv", "json", "excel" |
| `missing_value_strategy` | How to handle missing values | "mean" | "mean", "median", "mode", "drop" |
| `remove_outliers` | Whether to remove outliers | false | true, false |

### Model Configuration

#### Supported Algorithms

**Classification**:
- `RandomForest` - Random Forest Classifier
- `Logistic` - Logistic Regression
- `SVM` - Support Vector Machine

**Regression**:
- `RandomForest` - Random Forest Regressor
- `Linear` - Linear Regression
- `SVM` - Support Vector Regression

#### Model Parameters

| Parameter | Description | Default |
|-----------|-------------|---------|
| `algorithm` | Algorithm to use | Required |
| `type` | Model type (classification/regression) | Required |
| `n_estimators` | Number of estimators (for ensemble methods) | 100 |
| `max_depth` | Maximum tree depth | 10 |
| `random_state` | Random seed for reproducibility | 42 |
| `cv_folds` | Cross-validation folds | 5 |

### Monitoring Configuration

| Parameter | Description | Default |
|-----------|-------------|---------|
| `log_interval` | Monitoring interval in seconds | 5 |
| `drift_detection` | Enable data drift detection | true |
| `alerting` | Enable alerting system | true |
| `alert_threshold` | Alert threshold for metrics | 0.8 |

## Usage Examples

### Basic Usage

```python
from modules.data_pipeline import DataPipeline
from modules.model_trainer import ModelTrainer

# Configure components
config = {
    'data_path': 'data/my_data.csv',
    'algorithm': 'RandomForest',
    'type': 'classification'
}

# Initialize and run pipeline
pipeline = DataPipeline(config)
pipeline.initialize()
features, target = pipeline.process()

# Train model
trainer = ModelTrainer(config)
trainer.initialize()
trainer.train(features, target)
```

### Complete Workflow

```python
# Run the complete example
python examples/example_usage.py --mode complete
```

### Custom Data Processing

```python
# Load your own data
import pandas as pd

# Preprocess your data
data = pd.read_csv('your_data.csv')
# ... preprocessing steps ...

# Use with Aurora pipeline
pipeline = DataPipeline(config)
pipeline.initialize()
features, target = pipeline.process(data)  # Pass preprocessed data
```

## API Reference

### DataPipeline Class

#### Methods

- `initialize()` - Initialize the pipeline
- `process(data=None)` - Process data (load if None)
- `load_data()` - Load data from configured path
- `preprocess_data(data)` - Preprocess raw data
- `split_data(features, target)` - Split into train/test
- `get_data_summary()` - Get data statistics

#### Example

```python
pipeline = DataPipeline(config)
if pipeline.initialize():
    features, target = pipeline.process()
    X_train, X_test, y_train, y_test = pipeline.split_data(features, target)
```

### ModelTrainer Class

#### Methods

- `initialize()` - Initialize the trainer
- `train(X, y, optimize_hyperparameters=True)` - Train model
- `predict(X)` - Make predictions
- `predict_proba(X)` - Get probabilities (classification)
- `save_model(path=None)` - Save trained model
- `load_model(path)` - Load saved model
- `get_feature_importance()` - Get feature importance

#### Example

```python
trainer = ModelTrainer(config)
if trainer.initialize():
    results = trainer.train(X_train, y_train)
    predictions = trainer.predict(X_test)
    trainer.save_model()
```

### ModelMonitor Class

#### Methods

- `initialize()` - Initialize monitoring
- `start_monitoring(model=None)` - Start continuous monitoring
- `stop_monitoring()` - Stop monitoring
- `record_model_performance(y_true, y_pred, model_type)` - Record metrics
- `detect_drift(current_data, reference_data)` - Detect data drift
- `generate_report()` - Generate monitoring report

#### Example

```python
monitor = ModelMonitor(config)
if monitor.initialize():
    monitor.start_monitoring()
    performance = monitor.record_model_performance(y_test, predictions)
    report = monitor.generate_report()
```

### InferenceService Class

#### Methods

- `initialize()` - Initialize service
- `start_service()` - Start REST API server
- `stop_service()` - Stop server
- `predict_single(features)` - Make single prediction
- `get_service_info()` - Get service information

#### API Endpoints

- `GET /health` - Health check
- `POST /predict` - Make predictions
- `POST /predict_proba` - Get probabilities
- `GET /stats` - Service statistics
- `GET /history` - Prediction history

#### Example

```python
service = InferenceService(config)
if service.initialize():
    service.start_service()
    # Service now available at http://localhost:5000
```

## Data Format Requirements

### Input Data Format

1. **CSV Format** (recommended):
   - First row should contain column headers
   - Target column should be the last column
   - No missing values in target column

2. **JSON Format**:
   - Array of objects with consistent keys
   - Each object represents one data point

3. **Excel Format**:
   - First sheet used by default
   - First row should contain headers

### Data Quality Guidelines

1. **Missing Values**:
   - Configure handling strategy in config
   - Avoid missing values in target column

2. **Categorical Variables**:
   - Automatically encoded as integers
   - Consistent encoding across train/test

3. **Numerical Variables**:
   - Automatically scaled using StandardScaler
   - Outliers handled if enabled

4. **Target Variable**:
   - For classification: integer labels (0, 1, 2...)
   - For regression: continuous values

## Monitoring and Alerting

### Metrics Tracked

#### Model Performance
- Classification: Accuracy, Precision, Recall, F1-Score
- Regression: MSE, RMSE, R²

#### System Metrics
- CPU usage percentage
- Memory usage percentage
- Disk usage percentage
- Network I/O

#### Data Drift
- Feature distribution changes
- Statistical tests for drift detection

### Alert Types

#### System Alerts
- High CPU usage (>80%)
- High memory usage (>85%)
- Low disk space (<10%)

#### Performance Alerts
- Model performance degradation
- Training failures
- Prediction errors

#### Data Drift Alerts
- Significant feature distribution changes
- Data quality issues

### Custom Alert Callbacks

```python
def custom_alert_handler(alert):
    print(f"ALERT: {alert['message']}")
    # Send to external system, email, etc.

monitor = ModelMonitor(config)
monitor.add_alert_callback(custom_alert_handler)
```

## Troubleshooting

### Common Issues

#### 1. Configuration Errors
**Problem**: Missing required configuration keys
**Solution**: Check config.yaml has all required sections
```bash
python examples/example_usage.py --mode quick
```

#### 2. Data Loading Issues
**Problem**: Cannot find or read data file
**Solution**: Verify data path and format
```python
# Check file exists and is readable
import os
print(os.path.exists(config['data_path']))
```

#### 3. Model Training Failures
**Problem**: Training fails with errors
**Solution**: Check data quality and configuration
```python
# Validate data
pipeline = DataPipeline(config)
pipeline.initialize()
features, target = pipeline.process()
print(f"Features shape: {features.shape}")
print(f"Target distribution: {target.value_counts()}")
```

#### 4. Memory Issues
**Problem**: Out of memory errors
**Solution**: Reduce data size or adjust batch size
```yaml
# In config.yaml
model:
  batch_size: 16  # Reduce from default 32
```

#### 5. Port Conflicts
**Problem**: API server won't start
**Solution**: Change port in configuration
```yaml
api_server:
  port: 8081  # Use different port
```

### Debug Mode

Enable debug logging:
```yaml
logging:
  level: DEBUG
```

### Getting Help

1. Check the logs in `logs/` directory
2. Run the quick test to verify installation
3. Check the example usage for reference
4. Review the architecture documentation

## Best Practices

### 1. Data Preparation
- Clean data before processing
- Handle missing values appropriately
- Ensure consistent feature encoding

### 2. Model Training
- Use cross-validation for robust evaluation
- Monitor training progress
- Save models with metadata

### 3. Production Deployment
- Monitor model performance continuously
- Set up appropriate alerting
- Plan for model retraining

### 4. Configuration Management
- Use environment-specific configs
- Secure sensitive information
- Version control configuration changes

## Advanced Features

### Custom Components

Create custom components by inheriting from base classes:

```python
from core.base import BaseDataProcessor

class CustomProcessor(BaseDataProcessor):
    def initialize(self):
        # Custom initialization
        return True
    
    def process(self, data):
        # Custom processing logic
        return processed_data
    
    def cleanup(self):
        # Custom cleanup
        pass
```

### Hyperparameter Optimization

Enable advanced optimization:
```yaml
model:
  optimize_hyperparameters: true
  optimization_method: "grid_search"  # or "random_search"
  cv_folds: 10
```

### Ensemble Methods

Combine multiple models:
```python
# Train multiple models
models = []
for algorithm in ['RandomForest', 'Logistic', 'SVM']:
    config['model']['algorithm'] = algorithm
    trainer = ModelTrainer(config)
    trainer.initialize()
    trainer.train(X_train, y_train)
    models.append(trainer)

# Ensemble predictions
predictions = []
for model in models:
    pred = model.predict(X_test)
    predictions.append(pred)

# Average predictions
ensemble_pred = np.mean(predictions, axis=0)
```

## Performance Optimization

### 1. Data Optimization
- Use appropriate data types
- Remove unnecessary features
- Optimize memory usage

### 2. Model Optimization
- Choose appropriate algorithm
- Tune hyperparameters
- Use feature selection

### 3. System Optimization
- Monitor resource usage
- Optimize batch sizes
- Use caching appropriately

## Integration Examples

### Flask Web Application

```python
from flask import Flask, request, jsonify
from modules.inference_service import InferenceService

app = Flask(__name__)
service = InferenceService(config)
service.initialize()

@app.route('/predict', methods=['POST'])
def predict():
    data = request.get_json()
    prediction = service.predict_single(data['features'])
    return jsonify({'prediction': prediction.tolist()})
```

### Batch Processing

```python
# Process multiple datasets
datasets = ['data1.csv', 'data2.csv', 'data3.csv']
results = {}

for dataset in datasets:
    config['data_pipeline']['data_path'] = dataset
    pipeline = DataPipeline(config['data_pipeline'])
    pipeline.initialize()
    features, target = pipeline.process()
    
    trainer = ModelTrainer(config['model'])
    trainer.initialize()
    results[dataset] = trainer.train(features, target)
```

### Scheduled Retraining

```python
import schedule
import time

def retrain_model():
    # Load latest data
    # Retrain model
    # Update production model
    pass

# Schedule daily retraining
schedule.every().day.at("02:00").do(retrain_model)

while True:
    schedule.run_pending()
    time.sleep(60)
```