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This project contains the production-ready Machine Learning solution for detecting and classifying Covid-19, Viral disease, and No disease in posteroanterior and anteroposterior views of chest x-ray
𝗠𝗟 𝗽𝗿𝗼𝗷𝗲𝗰𝘁, encompassing key topics like 𝗗𝗮𝗴𝘀𝗵𝘂b and 𝗠𝗟𝗳𝗹𝗼𝘄 for version control, 𝗠𝗟𝗢𝗽𝘀 practices for efficient deployment, and robust 𝗖𝗜/𝗖𝗗 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲 setup. Showcased 𝗔𝗪𝗦 𝗱𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁 with the help of 𝗚𝗶𝘁𝗛𝘂𝗯 𝗔𝗰𝘁𝗶𝗼𝗻 prowess for seamless machine learning application integration.
An end-to-end deep learning project using DVC(MLOps Tool for Pipeline Tracking & Implementation) and Mlflow(MLOps Tool for Experiment Tracking and Model Registration) - Kidney Disease 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.
This project focuses on forecasting customer churn in the telecom industry by leveraging various features. The goal is to implement a straightforward, real-time prediction system capable of handling both batch and online predictions. The predictive model is deployed using Streamlit, providing an interactive and user-friendly interface for exploring
A comprehensive end-to-end Machine Learning project designed to predict bank deposit subscriptions using the well-known "Bank Marketing" dataset with production grade deployment techniques.