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.

This project aims to predict the quality of wines using various machine learning algorithms. It utilizes the MLflow platform to manage the end-to-end machine learning lifecycle, including data preprocessing, model training, hyperparameter tuning, and deployment on AWS EC2.

End to End Data science workflow for Car Price prediction

An end-to-end machine learning project with MLflow(MLOps Tool for Experiment Tracking and Model Registration) - Wine Quality Predictor