Deploying CatBoost Machine Learning Model as Web App on AWS

Data Information

Attribute Information:

• age: (numeric)
• job: type of job (categorical)
• marital: marital status (categorical: 'divorced','married','single','unknown'; note: 'divorced' means divorced or widowed)
• education: (categorical) default: has credit in default? (categorical: 'no','yes','unknown')
• housing: has housing loan? (categorical: 'no','yes','unknown')
• loan: has personal loan? (categorical: 'no','yes','unknown')
• balance: deposit amount (numeric)

Workflow Architecture

Requirements

• Python 3.8
• Docker
• Amazon Web Services account

Quick Start

• Clone the repository

git clone https://github.com/amine-akrout/bank_deposit_prediction

• Create a virtual and install requirements

python -m venv
pip install -r requirements.txt

• Train Catboost using Pycaret and log metrics and artifacts with MLflow

python ./model.py

MLflow experiment

Test locally

To test the web app locally using docker, start by building the image from the Dockerfile

docker build --pull --rm -f "Dockerfile" -t bankdeposit:latest "."

docker run -p 8080:8080 bankdeposit

the Web app should be runnining on http://localhost:8080/

Deploy to AWS ECS

CI/CD workflow

Using Github actions and aws.yml, we could continuously deploy the web app by simply using the term "deploy" in the commit message when pushing to main branch

Demo