End-to-end MLOps pipeline for churn prediction in live games.

End-to-end MLOps pipeline for automated concept drift detection, model retraining, and churn prediction.

📈 Build a production-ready churn prediction system with an end-to-end MLOps pipeline using FastAPI, scikit-learn, and automated CI/CD practices.

An end-to-end machine learning pipeline that predicts customer churn using the Telco dataset. Built with a clean Cookiecutter project structure. It includes EDA, preprocessing, and a Random Forest model hitting ~88% test accuracy. The app is fully containerized with Docker, CI/CD-ready using GitHub Actions + Render for automatic deployment.

Production MLOps pipeline: Customer churn prediction with Azure Databricks, MLflow, Docker, AKS, and CI/CD. Demonstrates end-to-end ML deployment for enterprise engineering.

End-to-end MLOps pipeline for Bank Customer Churn Prediction using DVC for data versioning, MLflow for experiment tracking, XGBoost for modeling, and Streamlit for an interactive prediction dashboard.

🚀 Educational MLOps project with GitHub Actions, Databricks Free Edition & MLflow. End-to-end automated pipeline: data prep, training, evaluation & model registry. Use case: customer churn prediction. 100% free & ready to teach.

Production-ready MLOps project for customer churn prediction featuring end-to-end ML pipeline, Docker containerization, DVC-based data & model versioning, and automated CI/CD with GitHub Actions for reproducible training, testing, and deployment.

This project is an end-to-end MLOps pipeline for Customer Churn Prediction. It includes data preprocessing, feature engineering, model training using a Random Forest classifier, and performance evaluation (accuracy, precision, recall, ROC-AUC).

End-to-end MLOps pipeline for telecom churn prediction. Features XGBoost model training, MLflow tracking and registry, FastAPI serving, Streamlit dashboard, and Docker orchestration. Demonstrates production ML workflows with model versioning, monitoring, and automated retraining capabilities.

End-to-end MLOps pipeline for e-commerce customer analytics. It uses the Online Retail II dataset to run RFM segmentation, churn prediction, and CLV modeling on Spark. Airflow orchestrates the workflow, MLflow tracks experiments and models, DVC versions data, and Streamlit provides an interactive UI—services are containerized with Docker.

An end-to-end MLOps churn prediction pipeline.

End-to-end MLOps pipeline for customer churn prediction

End-to-End MLOps Pipeline for Customer Churn Prediction.

End-to-end MLOps pipeline for customer churn prediction

End-to-End MLOps pipeline for Telco Churn prediction

End to end MLOps pipeline for Customer Churn Prediction

End-to-end MLOps pipeline for customer churn prediction

End-to-End MLOps Pipeline for Customer Churn Prediction

End-to-end MLOps pipeline for customer churn prediction.

Predicting if customer will churn or not and deploying the end to end code to AWS for production.

ChurnSight: End-to-End MLOps Pipeline for Customer Churn Prediction

End-to-end MLOps pipeline for Telco Customer Churn prediction

This project implements a complete end-to-end MLOps pipeline for customer churn prediction on the AWS cloud. It automates the entire machine learning lifecycle, from data ingestion and model training to deployment and monitoring. The architecture is designed to be robust, scalable, and maintainable, leveraging a modern MLOps stack.

End-to-end MLOps pipeline for Telco customer churn prediction.

End-to-end MLOps pipeline for energy utility customer churn prediction.

End-to-end MLOps pipeline for real-time customer churn prediction.

End-to-end MLOps pipeline for customer churn prediction (Telco dataset)

Project Title: End-to-End MLOps Pipeline for Customer Churn Prediction

End-to-end MLOps pipeline for churn prediction using MLflow, FastAPI & Docker.