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.

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.

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

Quizzy is an AI interviewer that creates hyper-personalized interview simulation using a RAG-based system for dynamic conversations. It analyzes emotions, perception, posture, and responses, ensuring a natural flow. With job opening scraping and an embedding-based ATS score checker, Quizzy prepares you for the job market. Built with MLOps in Django

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.

A rent prediction webapp, based on Delhi's dataset. Includes complete Pytorch model, CI/CD pipeline along with MlFlow and AWS !

End to End Data science workflow for Car Price prediction

Bone marrow transplants can be life-saving, but predicting patient survival is complex. In this project, I used machine learning to analyze key medical factors and improve survival predictions. I also implemented CI/CD pipelines, used MLflow for model tracking, and deployed the model on an AWS EC2 instance.

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

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

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.

This project utilizes the UCI Wine Quality Dataset to demonstrate an end-to-end machine learning pipeline, with deployment on AWS. It incorporates MLflow for experiment tracking, as well as (CI/CD) through GitHub Actions

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

End to End Wine Quality Prediction

Wine Quality Prediction using ElasticNet Regression using MLFlows

A simple MLOPs project, that predicts the quality of some wine. Implemented Mlflow for experiment tracking, created a Docker Image of the app and deployed the app on Render

this project is used to find the wine quality it buid wih mlflow and aws with cicd .

This is advance machine learning operation pipelines integrated with MLflow to monitor artifacts and metrices. Deployed in AWS via CICD GitHub Actions.

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