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.

A MLOps project uses DagsHub & MLFlow

Testing MLflow & Dagshub open-source platform collaboration.

Automated Tool for Optimized Modelling

MLFlow example on Dagshub and AWS

All the available resources to master MLOPS from scratch

Introduction to DagsHub and its integration to kedro

This project utilizes cutting-edge MLOps practices, integrating CI/CD pipelines, DVC, and MLflow to streamline development, testing, and deployment for deep learning models on chest CT images.

Mapping the diverse hues of credit risk into risk categories for informed decision-making

Automating machine learning experiment tracking with MLFlow on AWS and Dagshub.

This project applies machine learning models to predict BMI categories based on individual physical attributes, utilizing MLflow for experiment tracking and model management, with integration into DagsHub for collaborative data science workflows. It showcases the power of MLflow in enhancing model lifecycle management and reproducibility.

RuralCredit: Empowering Tomorrow's Opportunities

A Deep Learning project to detect and classify INR coins

End to End Kidney Disease Classification

Getting familiar with mflow

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.

PulmoDetect Image Analysis

Chest Cancer Classification using Chest CT Scan. Using Vgg16 Model.

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