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End-to-End Machine Learning project aimed at classifying diseases from medical images. This project uses DVC (Data Version Control) to manage the pipelines and the VGG16 model for image classification.
Advanced predictive model for box office revenue. With precision forecasting and confidence-building insights, our solution empowers production houses to optimize resources and maximize profitability.
This repository offers a comprehensive solution for chest disease detection, covering data ingestion, preprocessing, model training, and CI/CD deployment pipelines. From raw data to automated deployment, streamline your chest disease detection process with our end-to-end solution.
Build end-to-end DL pipeline for computer vision (Image classification) for “Chest Disease Classification from Chest CT Scan Images” and deploy Flask web app to AWS EC2 with Docker and CI/CD tool: Jenkins
Implemented research paper on UNETR on a custom multi-class dataset, built modular pipelines, served the model as REST API, developed the backend on Django REST Framework, deployed on AWS, developed frontend on Next.Js, deployed on Vercel. Implemented MLOps and DevOps.
Implementation of MLops pipeline for Chest Disease Classification from Chest CT Scan Images using computer vision Vgg16 pretrained Image classification model. further perform deployment on AWS EC2 using Docker, CI/CD Jenkins tool, using Flask as front end interface.
This project explains how to automate data and machine learning pipelines using various tools, such as DVC, OmegaConf, Hydra, and MLFlow to achieve that.