Developed a web application that predicts the quality of wines based on various features using machine learning techniques. The application will be built using the Flask framework, and it will integrate MLflow for efficient experiment tracking and model management.

End to End Wine Quality Prediction

This project is about the prediction of red wine quality using different machine learning algorithms with MLOps and CICD pipeline.

ML engineering with mlflow-kedro

Wine Quality Prediction using ElasticNet Regression using MLFlows

end to end project using mlflow

수어 사진을 MLflow에서 관리하는 모델을 통해 예측한 후 GPT로 응답 문장을 생성

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

Training and deploying CatBoost Model with MLFlow, Flask, AWS ECS and github actions

End to End MLops with Mlflow project with Github actions and docker image

End to End Data science workflow for Car Price prediction

Training and deploying LightGBM Model with MLFlow, fastapi, App Engine and github actions

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.

Implemented a wine quality prediction project using MLOps and MLflow. Utilized the Wine Quality dataset, developed machine learning models, and deployed them on an EC2 instance. This project aimed to gain hands-on experience in MLOps principles and the effective use of MLflow for model tracking and deployment.

Deploying an end-to-end ml/dl model (for predicting maintaince for aircrafts by using dataset provided by NASA) into cloud server using Flask and Docker with CI/CD pipeline

This repository contains instructions, template source code and examples on how to serve/deploy machine learning models using various frameworks and applications such as Docker, Flask, FastAPI, BentoML, Streamlit, MLflow and even code on how to deploy your machine learning model as an android app.