The project is a concoction of research (audio signal processing, keyword spotting, ASR), development (audio data processing, deep neural network training, evaluation) and deployment (building model artifacts, web app development, docker, cloud PaaS) by integrating CI/CD pipelines with automated tests and releases.

This repository serves as a showcase for my data science projects, demonstrating a project on classification on HDPE and PET plastic bottle waste using convolution neural networks

The Chest Cancer Classification project diagnoses chest cancer from medical images using deep learning. It integrates MLflow for experiment tracking, DVC for version control, and Flask for backend processing. Docker and a CI/CD pipeline with GitHub Actions and AWS.

This project is a concoction of research (audio signal processing, keyword spotting, ASR), development (audio data processing, deep neural network training, evaluation) and deployment (building model artifacts, web app development, docker) by integrating CI/CD pipelines with automated releases and tests.

This repository exemplifies a robust ML workflow, leveraging MLflow for experiment tracking, Docker for containerization, TensorFlow Serving for model deployment, and GitHub Actions for CI/CD. It embodies a comprehensive system designed to predict diabetes progression using advanced machine learning paradigms.

This project leverages the VGG-16 CNN model for chest cancer classification, with a modular pipeline utilizing MLflow, DVC, Docker, and GitHub Actions, deployed on AWS EC2

Using ML FLOW (an open source platform for the machine learning lifecycle to do) a classification problem

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