MLOps End-to-End: Customer Churn Prediction System

A production-grade MLOps pipeline for predicting telecom customer churn, featuring automated data preprocessing, ML model training, experiment tracking with MLflow, distributed training using PySpark, real-time inference via Kafka streaming, Airflow DAG orchestration, and Dockerized REST API deployment.

Churn prediction for telco customer. To manage end to end MLOps lifecycle

Customer Churn Prediction project with full MLOps pipeline

This is an AWS MLE and MLOps Bank Customers Churn Prediction Project.

This is a comprehensive project focused on understanding and predicting customer churn in the telecommunications industry. Utilizing advanced data analysis and machine learning techniques, this project aims to provide insights into customer behavior and help develop effective strategies for customer retention.

Capstone Project for DataTalksClub MLOps Zoomcamp 2025 Course

A CI/CD-enabled MLOps pipeline for predicting customer churn, covering automated model training, deployment, and continuous monitoring.

Production-grade MLOps pipeline for customer churn prediction with automated training, validation, and serving. Built with Airflow, MLflow, MinIO, Evidently AI, and FastAPI.

MLOps pipeline for customer churn case study - CodePro is an EdTech startup that had a phenomenal seed A funding round. It used the money to increase its brand awareness. As the marketing spend increased, it got several leads from different sources. Although it had spent significant money on acquiring customers, it had to be profitable in the long run to sustain the business. The major cost that the company is incurring is the customer acquisition cost (CAC). customer acquisition cost is required to be high in companies. But as their businesses grow, these companies start focussing on profitability. Many companies first offer their services for free or provide offers at the initial stages but later start charging customers for these services. For example, Google Pay used to provide many offers, and Reliance Jio in India offered free mobile data services for over a year. Once these brands were established and brand awareness was generated, these businesses started growing organically. At this point, they began charging customers. Businesses want to reduce their customer acquisition costs in the long run. There are many ways to do that. You will learn about these methods in the next segment.

Production-ready ML pipeline for telco customer churn prediction using advanced ensemble methods (XGBoost, CatBoost, Random Forest). Handles class imbalance, provides business insights, and includes modular MLOps architecture. Built with scikit-learn, featuring comprehensive EDA, feature engineering, and business impact analysis.

End-to-end MLOps platform for customer churn prediction featuring feature stores, model versioning, API serving, and automated workflows

End-to-end MLOps project for telecom customer churn prediction using Scikit-Learn, MLflow, Streamlit, and Docker.

“End-to-end Dockerized MLOps project with FastAPI + Scikit-Learn”

End-to-end ML project to predict customer churn with CI/CD, Docker, and MLflow.

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Customer Churn MLOps is an end-to-end machine learning pipeline for predicting customer churn using tabular data. It integrates DVC for data/model versioning, MLflow for experiment tracking, FastAPI for model serving, and GitHub Actions for CI/CD automation, making the project fully production-ready.

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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 customer churn prediction pipeline with MLflow experiment tracking, model comparison, and Dockerized deployment.

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Proyecto de Machine Learning y MLOps para predecir el abandono (churn) de clientes en una empresa de telecomunicaciones. Incluye análisis exploratorio, entrenamiento de modelos, interpretabilidad con LIME y despliegue con FastAPI y Docker.

End-to-end customer churn prediction pipeline built with Apache Spark MLlib — featuring synthetic data generation, feature engineering, Chi-Square selection, and multi-model hyperparameter tuning with cross-validation.

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This project focuses on building a model to predict customer churn and integrating MLOps practices throughout the development lifecycle. We will cover data preprocessing, model training, evaluation, experiment tracking, model versioning, and a basic CI/CD pipeline.

pretraitement et entrainement

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This project leverages the ZenML MLOps framework to build, train, and deploy a machine learning pipeline for predicting customer churn. It automates data ingestion, preprocessing, model training, and evaluation to help businesses proactively identify customers likely to leave.

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.