A collection of end-to-end MLOps projects This repository demonstrates how to build, train, track, and deploy a models using DVC, MLflow, GitHub Actions, and AWS.

1. Update config.yaml
2. Update secrets.yaml [Optional]
3. Update params.yaml
4. Update the entity
5. Update the configuration manager in src/config
6. Update the components
7. Update the pipeline
8. Update the main.py
9. Update the dvc.yaml
10. Run app.py

git clone https://github.com/Yusuf-Abol/endtoend-mlops.git
cd endtoend-mlops/projects/covid_chest_classifier

conda create -n tubato_env python=3.11.13 -y
conda activate tubato_env

pip install -r requirements.txt

python app.py

Now, open your local host and port in your browser.

• MLflow Documentation
• Tutorial Video

Run MLflow locally:

mlflow ui

Or connect with DagsHub:

export MLFLOW_TRACKING_URI=https://dagshub.com/<username>/<repo>.mlflow
export MLFLOW_TRACKING_USERNAME=<username>
export MLFLOW_TRACKING_PASSWORD=<token>

dvc init
dvc repro
dvc dag

MLflow

• Production grade experiment tracking
• Logs, tags, and manages models

DVC

• Lightweight for experiments & pipelines
• Orchestrates ML workflows

• Access: EC2 + ECR

• Policies:

  • AmazonEC2ContainerRegistryFullAccess
  • AmazonEC2FullAccess

566373416292.dkr.ecr.us-east-1.amazonaws.com/chicken

Install Docker:

sudo apt-get update -y
sudo apt-get upgrade
curl -fsSL https://get.docker.com -o get-docker.sh
sudo sh get-docker.sh
sudo usermod -aG docker ubuntu
newgrp docker

Go to: Settings > Actions > Runner > New Self-Hosted Runner

AWS_ACCESS_KEY_ID=
AWS_SECRET_ACCESS_KEY=
AWS_REGION=us-east-1
AWS_ECR_LOGIN_URI=566373416292.dkr.ecr.ap-south-1.amazonaws.com
ECR_REPOSITORY_NAME=simple-app

✅ With this, you now have:

• End-to-end pipeline with configs + DVC
• Experiment tracking with MLflow
• CI/CD pipeline with GitHub Actions
• Cloud deployment with AWS