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Launch AI - AI/ML Service

AI and Machine Learning service for the Launch AI campaign management platform.

Features

• 🤖 Performance Prediction: ML models for campaign performance forecasting
• 🎯 Optimization Engine: AI-powered campaign optimization recommendations
• 📊 Insights Generation: Automated insights and anomaly detection
• 🔍 Audience Analysis: Advanced audience segmentation and targeting
• 📈 Trend Analysis: Temporal and seasonal performance patterns
• 🏆 Competitive Intelligence: Market positioning and competitive analysis

Technology Stack

• Flask 2.3 for API framework
• scikit-learn for machine learning models
• NumPy & Pandas for data processing
• Joblib for model serialization
• Python-dotenv for environment management

Getting Started

Prerequisites

• Python 3.8+
• Virtual environment recommended

Installation

# Create and activate virtual environment
python3 -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Start the AI service
python app.py

The AI service will be available at http://localhost:8000

Environment Variables

Create a .env file in the root directory:

FLASK_DEBUG=True
SECRET_KEY=ai-service-secret-key
MODEL_PATH=./models/

API Endpoints

Health Check

• GET /health/ - Service health status
• GET /health/ready - Readiness check with model status

Prediction & Analysis

• POST /api/predict - Predict campaign performance
• POST /api/analyze/audience - Analyze target audience
• GET /api/models/status - Get ML models status

Optimization

• POST /api/optimize - Get optimization recommendations
• POST /api/optimize/budget - Optimize budget allocation

Insights

• POST /api/insights - Generate campaign insights

Service Architecture

launch-ai/
├── api/
│   ├── routes.py        # Main API endpoints
│   └── health.py        # Health check endpoints
├── services/
│   ├── ml_service.py    # Machine learning operations
│   ├── optimization_service.py  # Optimization algorithms
│   └── insights_service.py      # Insights generation
├── models/              # Trained ML models storage
├── utils/               # Utility functions
└── app.py              # Flask application entry point

ML Models

Performance Predictor

• Purpose: Predict campaign performance metrics
• Features: Platform, budget, campaign type, demographics
• Outputs: Impressions, clicks, conversions, CTR, CPC predictions

Audience Analyzer

• Purpose: Analyze and segment target audiences
• Features: Demographic data, behavioral patterns, engagement history
• Outputs: Audience segments, engagement scores, optimal timing

Optimization Engine

• Purpose: Generate optimization recommendations
• Algorithms: Budget allocation, targeting refinement, bidding strategies
• Outputs: Actionable recommendations with expected impact

Data Processing

Input Data Format

{
  "campaign_id": "string",
  "platform": "Meta|Google",
  "budget": "number",
  "target_audience": {
    "demographics": {},
    "interests": [],
    "behaviors": []
  },
  "historical_performance": {
    "impressions": "number",
    "clicks": "number",
    "conversions": "number"
  }
}

Output Format

{
  "success": true,
  "data": {
    "predictions": {},
    "recommendations": [],
    "insights": [],
    "confidence_score": "number"
  }
}

Model Training

Training Pipeline

1. Data Collection: Gather campaign performance data
2. Feature Engineering: Extract relevant features
3. Model Training: Train ML models with cross-validation
4. Model Evaluation: Assess model performance
5. Model Deployment: Save and deploy trained models

Model Updates

• Models are retrained weekly with new data
• A/B testing for model performance comparison
• Gradual rollout of updated models

Performance Monitoring

Metrics Tracked

• Prediction Accuracy: MAE, RMSE for performance predictions
• Recommendation Effectiveness: Uplift from implemented recommendations
• Service Performance: Response times, error rates
• Model Drift: Feature distribution changes over time

Alerting

• Model performance degradation alerts
• Service availability monitoring
• Anomaly detection in predictions

Development

Adding New Models

1. Create model class in models/ directory
2. Implement training and prediction methods
3. Add model loading to ml_service.py
4. Create API endpoints in routes.py
5. Add tests for new functionality

Testing

# Install test dependencies
pip install pytest pytest-flask

# Run tests
pytest tests/

# Run with coverage
pytest --cov=services tests/

Deployment

Production Setup

# Install production server
pip install gunicorn

# Run with Gunicorn
gunicorn -w 4 -b 0.0.0.0:8000 app:create_app()

Docker Support

FROM python:3.9-slim
WORKDIR /app
COPY requirements.txt .
RUN pip install -r requirements.txt
COPY . .
EXPOSE 8000
CMD ["gunicorn", "-w", "4", "-b", "0.0.0.0:8000", "app:create_app()"]

Model Storage

• Models stored in models/ directory
• Version control for model artifacts
• Backup and recovery procedures

Security

• Input validation for all API endpoints
• Rate limiting for resource-intensive operations
• Model access controls
• Secure model artifact storage

Contributing

1. Follow scikit-learn conventions for ML code
2. Document all model parameters and outputs
3. Add comprehensive tests for new models
4. Update API documentation for new endpoints
5. Monitor model performance after deployment =======

launch-ai

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