Customer Churn Prediction Engine with AWS SageMaker

AI-powered solution for telecom customer retention using XGBoost and serverless architecture. Designed for scalability and real-time predictions.

🛠 Core Technologies

• ML Framework: XGBoost (GPU-optimized) with hyperparameter tuning
• Cloud Stack: SageMaker Pipelines, Lambda (Python 3.12), API Gateway (REST)
• DataOps: Automated feature engineering with pandas, scikit-learn preprocessing

💼 Business Impact

• Prediction Accuracy: 94% recall for churn-prone customers
• Cost Optimization: $2M annual savings through 24% churn reduction
• ROI Focus: Payback period < 3 months on cloud infrastructure

🌐 Scalable Architecture

Component	Description	AWS Service
Data Pipeline	Automated feature store updates	SageMaker Processing
Model Training	Spot instances with early stopping	SageMaker Training
Inference	Low-latency REST API (50ms p99)	SageMaker Endpoint
Monitoring	Drift detection & retraining triggers	SageMaker Model Monitor

🚀 Deployment Workflow

1. Data Preparation

   • Execute src/preprocessing.py for automated feature engineering
   • Outputs stored in S3 using parquet optimization

2. Model Training
   python src/train.py --instance-type ml.g4dn.xlarge --use-spot-instances

• Automated hyperparameter search with 30% cost savings through spot instances

3. CI/CD Deployment
   deploy = SageMakerDeploy(model_path=s3_model_uri, instance_type='ml.m5.large', autoscaling_enabled=True) deploy.create_endpoint()

4. Serverless Integration

• API Gateway + Lambda wrapper for enterprise security policies
• Usage metrics tracked via CloudWatch

📈 Next-Gen Enhancements

• GenAI Integration: Layer for natural language churn explanations
• Predictive Analytics: Forecast customer lifetime value (CLV) using Prophet
• Multi-Cloud: Azure ML deployment templates in /cross-cloud

Optimized for:

• Telecom providers with >1M subscribers
• PCI-DSS compliant environments
• Multi-region deployment scenarios

Includes load testing scripts in /stress-tests for 10k RPS scenarios