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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. For API reference, check our API Documentation.

🚀 Installation: Complete installation instructions available in our Installation Guide.

🔧 Configuration: Detailed configuration options in our Configuration Guide.

🌐 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:

    pip install -r requirements.txt

  3. Verify installation:

    python examples/example_usage.py --mode quick

💡 Tip: For detailed installation instructions, including system requirements and troubleshooting, see our Installation Guide.

Quick Start

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

🔍 Monitoring: After starting, monitor your system with our Monitoring Guide.

Configuration

Main Configuration File (config/config.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

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

Custom Data Processing

# 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

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

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

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

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

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

python examples/example_usage.py --mode quick

2. Data Loading Issues

Problem: Cannot find or read data file Solution: Verify data path and format

# 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

# 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

# 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

api_server:
  port: 8081  # Use different port

Debug Mode

Enable debug logging:

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:

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:

model:
  optimize_hyperparameters: true
  optimization_method: "grid_search"  # or "random_search"
  cv_folds: 10

Ensemble Methods

Combine multiple models:

# 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

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

# 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

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)

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