MLOps Pipeline with + + +

A complete Machine Learning lifecycle. The pipeline is as follows:

1. Read Data➙2. Split train-test➙3. Preprocess Data➙4. Train Model➙
      ➙ 5.1 Register Model
      ➙ 5.2 Update Registered Model

Telco Customer Churn dataset from Kaggle.

Tech Stack

: For experiment tracking and model registration
: Store the MLflow tracking
: Store the registered MLflow models and artifacts
: Orchestrate the MLOps pipeline
: Machine Learning
: R&D

How to reproduce

1. Have Docker installed and running.

Make sure docker-compose is installed:

pip install docker-compose

2. Clone the repository to your machine.

git clone https://github.com/Deffro/MLOps.git

3. Rename .env_sample to .env and change the following variables:

   • AWS_ACCESS_KEY_ID
   • AWS_SECRET_ACCESS_KEY
   • AWS_REGION
   • AWS_BUCKET_NAME

4. Run the docker-compose file

docker-compose up --build -d

Urls to access

• http://localhost:8080 for Airflow. Use credentials: airflow/airflow
• http://localhost:5000 for MLflow.
• http://localhost:8893 for Jupyter Lab. Use token: mlops

Cleanup

Run the following to stop all running docker containers through docker compose

docker-compose stop

or run the following to stop and delete all running docker containers through docker

docker stop $(docker ps -q)
docker rm $(docker ps -aq)

Finally, run the following to delete all (named) volumes

docker volume rm $(docker volume ls -q)