premkumar246 / End-to-End-Kidney-Disease-Classification

This repository holds all the project files belongs to a Kidney diesease classification application which takes x-rays images and classify the image as dieseased or healthy by using Deep learning CNN classification techniques.

End-to-End-Kidney-Disease-Classification-Application

This repository holds all the project files belongs to a Kidney diesease classification application which takes x-rays images and classify the image as dieseased or healthy by using Deep learning CNN classification techniques.

Process

1. GitHub Repository setup.
2. Project template creation.
3. Project setup & requirements installation
4. Logging, Utils & Exception modules
5. Project Workflows
6. All components notebook experiements
7. All componenet modular code implimentation
8. Training Pipeline
9. MLflow - for experiments tracking & Model registration
10. DVC - For pipeline tracking & implementation
11. Prediction pipeline & User App Creation
12. Dockerization
13. CI/CD deployement in AWS

step 01 | GitHub Repository Setup

1. Goto GitHub, click on new repository and name, description and readme.md file and create the repo. copy the http url of the repo.
2. Now create a folder in your local machine, go to terminal use below command to clone the repo in to your local machine.

git clone <htts url of your git url>

3. Giev your username and password (token) to the terminal and clone the repo.
4. Once the repo is cloned then open the folder in vscode though terminal using below command

code .

Step 02 | Project Template Creation

1. Create the project template b

Worksflows

1. update config.yaml
2. update secrets.yaml
3. update params.yaml
4. update the entity
5. update the configuration manager in src config
6. update the componets
7. update the pipeline
8. update the main.py
9. update the dvc.yaml
10. app.py