End-to-end pipeline for kidney disease classification

FIRAS TLILI

Data Scientist

Cloud Infrastructure Architect

ML Engineer

AWS

Python

TensorFlow

End-to-End-Kidney-Disease-Classification-With-MLFLOW-DVC-And-Deployment

This repository contains an end-to-end pipeline for kidney disease classification using machine learning. The project integrates MLflow for experiment tracking, DVC for data versioning, and a robust deployment process. It covers data preprocessing, model training, evaluation, and deployment to a cloud-based environment.

Workflows

Update config.yaml

Update secrets.yaml [Optional]

Update params.yaml

Update the entity

Update the configuration manager in src config

Update the components

Update the pipeline

Update the main.py

Update the dvc.yaml

app.py

How to run?

STEPS:

Clone the repository

https://github.com/TLILIFIRAS/End-to-End-Kidney-Disease-Classification-With-MLFLOW-DVC-And-Deployment

STEP 01- Create a conda environment after opening the repository

conda create -n cnncls python=3.8 -y

conda activate cnncls

STEP 02- install the requirements

pip install -r requirements.txt

# Finally run the following command
python app.py

Now,

open up you local host and port

MLflow

Documentation

cmd

mlflow ui

DVC cmd

dvc init

dvc repro

dvc dag

About MLflow & DVC

MLflow

Its Production Grade

Trace all of your expriements

Logging & taging your model

DVC

Its very lite weight for POC only

lite weight expriements tracker

It can perform Orchestration (Creating Pipelines)

AWS-CICD-Deployment-with-Github-Actions

1. Login to AWS console.

2. Create IAM user for deployment

#with specific access

1. EC2 access : It is virtual machine

2. ECR: Elastic Container registry to save your docker image in aws


#Description: About the deployment

1. Build docker image of the source code

2. Push your docker image to ECR

3. Launch Your EC2 

4. Pull Your image from ECR in EC2

5. Lauch your docker image in EC2

#Policy:

1. AmazonEC2ContainerRegistryFullAccess

2. AmazonEC2FullAccess

3. Create ECR repo to store/save docker image

- Save the URI: 566373416292.dkr.ecr.us-east-1.amazonaws.com/chicken

4. Create EC2 machine (Ubuntu)

5. Open EC2 and Install docker in EC2 Machine:

#optinal

sudo apt-get update -y

sudo apt-get upgrade

#required

curl -fsSL https://get.docker.com -o get-docker.sh

sudo sh get-docker.sh

sudo usermod -aG docker ubuntu

newgrp docker

6. Configure EC2 as self-hosted runner:

setting>actions>runner>new self hosted runner> choose os> then run command one by one

7. Setup github secrets:

AWS_ACCESS_KEY_ID=

AWS_SECRET_ACCESS_KEY=

AWS_REGION = us-east-1

AWS_ECR_LOGIN_URI = demo>>  566373416292.dkr.ecr.ap-south-1.amazonaws.com

ECR_REPOSITORY_NAME = simple-app

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Posted Jan 23, 2025

This repository contains an end-to-end pipeline for kidney disease classification using machine learning. The project integrates MLflow for experiment tracking…

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