End-to-End MLOps Pipeline for Customer Segmentation
Dec 15, 2023
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2 min read
Built production-ready ML pipeline for customer segmentation with automated workflows, containerization, and cloud deployment. Demonstrates end-to-end MLOps practices from data engineering to model serving.
Overview
This project showcases modern MLOps practices by building a complete pipeline for customer segmentation - from feature engineering through deployment, with emphasis on reproducibility, automation, and scalability.
Feature Engineering & Modeling
Data Pipeline:
- Created synthetic customer dataset with advanced feature extraction
- Implemented RFM (Recency, Frequency, Monetary) features for behavior analysis
- Product diversity metrics and temporal patterns
Machine Learning:
- K-means clustering for customer segmentation
- Detailed customer profiles with behavioral insights
- Interpretable segments for business decision-making
MLOps Infrastructure
Workflow Orchestration:
- Apache Airflow for managing and scheduling ML workflows
- Modular DAGs for data processing, training, and evaluation
- Automated pipeline execution and monitoring
Containerization & CI/CD:
- Dockerized entire project for reproducibility
- Multi-stage Docker builds for optimized images
- CI/CD pipelines for automated testing and deployment
Cloud Deployment:
- Flask REST API for model serving
- Deployed on Google Cloud Platform
- Scalable inference endpoint for real-time predictions
Key Technologies
- Orchestration: Apache Airflow
- Containerization: Docker, Docker Compose
- Cloud: Google Cloud Platform (Cloud Run, Cloud Storage)
- API: Flask for model serving
- ML: scikit-learn, pandas, NumPy
Impact
Demonstrated how MLOps best practices enable reliable, reproducible, and scalable machine learning systems - bridging the gap between research code and production-ready solutions.
