This project is an end-to-end MLOps pipeline for predictive maintenance, focused on predicting machine failure using manufacturing sensor data. The entire pipeline is containerized using Docker, integrated with GitHub Actions for CI/CD, and deployed to Azure Web Services.

End-to-end MLOps project for predictive maintenance using engine sensor data. Includes data versioning on Hugging Face, MLflow experiment tracking, CI/CD with GitHub Actions, and Dockerized Streamlit deployment for real-time engine failure classification.

Aircraft components are susceptible to degradation, which affects directly their reliability and performance. This machine learning project will be directed to provide a framework for predicting the aircraft’s remaining useful life (RUL) based on the entire life cycle data in order to provide the necessary maintenance behavior.

End-to-end MLOps pipeline for predictive maintenance using sensor data. Features automated model training, drift detection, FastAPI deployment, and comprehensive monitoring with 92% accuracy in failure prediction.

A Predictive Maintenance Model with MLOps

MLOps pipeline for predictive maintenance with XGBoost baseline and LSTM temporal model

An end-to-end MLOps system that predicts aircraft engine Remaining Useful Life (RUL) using NASA C-MAPSS data, served via FastAPI, tracked with MLflow, monitored through Prometheus & Grafana, and deployed on AWS EC2 with CI/CD automation.

ML-powered predictive maintenance with full MLOps, such as TensorFlow, FastAPI, MLflow, Docker, CI/CD

End-to-end predictive maintenance system combining XGBoost, MLOps, and a RAG-based LLM assistant with Streamlit deployment.

This project involves working with the AI4I 2020 Predictive Maintenance Dataset, a synthetic dataset that simulates real-world industry scenarios for predictive maintenance. Objective: Develop an efficient and scalable MLOps pipeline for automating the machine learning workflow in predictive maintenance.

An award-winning, now open source platform for predictive industry maintenance. This project is built on a resilient and scalable stack including FastAPI, PostgreSQL/TimescaleDB , and Redis, all with Docker and with full cloud AWS EC2 deployment. It implements a Multi-Agent AI system and a MLOps lifecycle with MLflow to manage 17 distinct models

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This system predicts Remaining Useful Life (RUL) of machines and automatically detects when the model becomes unreliable due to data drift, triggering retraining

Production-ready MLOps pipeline for predictive maintenance with CI/CD

Production-Grade Predictive Maintenance ML System with MLOps (Training, API, Monitoring, Drift Detection, Auto Retraining)

Industrial Predictive Maintenance ML system with end-to-end MLOps, real-time inference API, monitoring, and CI/CD.

ML-based Predictive Maintenance Platform using XGBoost, AWS Glue, and SageMaker to reduce part failures and automate MLOps with SHAP explainability.

End-to-end predictive maintenance project with FastAPI, MLflow, Docker, and CI/CD demonstrates a complete MLOps workflow from training to deployment.

Predictive maintenance pipeline using NASA C-MAPSS data for RUL forecasting and anomaly detection, with Dockerized local deployment and MLOps-oriented project structure.

A production-ready predictive maintenance system to estimate the Remaining Useful Life (RUL) of NASA Turbofan engines. Built with Python, FastAPI, Docker, and MLOps. (Practice)

An end-to-end MLOps pipeline for vehicle predictive maintenance. Combines real-time anomaly detection with a RAG-powered vector database to instantly recommend historical maintenance fixes. Built with FastAPI, scikit-learn, ChromaDB, and automated CI/CD.

End-to-end MLOps pipeline for predictive maintenance using MLflow. Predicts equipment failures with NASA Turbofan data, leveraging Tracking, Projects, Models, and Registry. Deploys model as a REST API with Docker

End-to-end MLOps pipeline for predictive engine maintenance. Features data versioning (Hugging Face), automated CI/CD with GitHub Actions, model tuning (AdaBoost and others) with MLflow tracking, and containerized deployment using Docker and Streamlit on Hugging Face

End-to-end MLOps project for predictive maintenance using engine sensor data. Includes data versioning on Hugging Face, MLflow experiment tracking, CI/CD with GitHub Actions, and Dockerized Streamlit deployment for real-time engine failure classification.

Cloud-native predictive maintenance ML pipeline with automated DataOps and MLOps workflows. Built on NASA C-MAPSS turbofan engine dataset featuring time-series feature engineering, real-time monitoring, and AWS integration for industrial equipment failure prediction.

A cloud-native Predictive Maintenance (PdM) ecosystem featuring a Python-based Digital Twin, an AWS MLOps pipeline, and real-time Random Forest inference. Engineered with a decoupled microservices architecture (Docker, InfluxDB, MQTT) and validated through an automated Edge Chaos Engine.

A production-grade, open-source SaaS platform for predictive maintenance. This project is built on a resilient and scalable stack including FastAPI, PostgreSQL/TimescaleDB , and Redis, all with Docker and with full cloud deployment. It implements a sophisticated Multi-Agent AI system and a MLOps lifecycle with MLflow to manage 17 distinct models