https://github.com/benitomartin/mlops-aws-stroke

MLOps Stroke Prediction
https://github.com/benitomartin/mlops-aws-stroke

aws aws-ecr aws-lambda aws-s3 docker flask-api jupyter-notebook python sagemaker

Last synced: 8 months ago
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MLOps Stroke Prediction

• Host: GitHub
• URL: https://github.com/benitomartin/mlops-aws-stroke
• Owner: benitomartin
• Created: 2024-05-27T10:30:32.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2024-06-05T06:58:25.000Z (over 1 year ago)
• Last Synced: 2024-12-31T14:28:46.016Z (9 months ago)
• Topics: aws, aws-ecr, aws-lambda, aws-s3, docker, flask-api, jupyter-notebook, python, sagemaker
• Language: Jupyter Notebook
• Homepage:
• Size: 905 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# MLOPS STROKE PREDICTION ⚱️



  



This is a personal MLOps project based on a [Kaggle](https://www.kaggle.com/datasets/fedesoriano/stroke-prediction-dataset) dataset for stroke prediction. 

Feel free to ⭐ and clone this repo 😉

## Tech Stack

![Visual Studio Code](https://img.shields.io/badge/Visual%20Studio%20Code-0078d7.svg?style=for-the-badge&logo=visual-studio-code&logoColor=white)

![Jupyter Notebook](https://img.shields.io/badge/jupyter-%23FA0F00.svg?style=for-the-badge&logo=jupyter&logoColor=white)

![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)

![Pandas](https://img.shields.io/badge/pandas-%23150458.svg?style=for-the-badge&logo=pandas&logoColor=white)

![NumPy](https://img.shields.io/badge/numpy-%23013243.svg?style=for-the-badge&logo=numpy&logoColor=white)

![Matplotlib](https://img.shields.io/badge/Matplotlib-%23d9ead3.svg?style=for-the-badge&logo=Matplotlib&logoColor=black)

![scikit-learn](https://img.shields.io/badge/scikit--learn-%23F7931E.svg?style=for-the-badge&logo=scikit-learn&logoColor=white)

![Flask](https://img.shields.io/badge/flask-%23000.svg?style=for-the-badge&logo=flask&logoColor=white)

![Docker](https://img.shields.io/badge/docker-%230db7ed.svg?style=for-the-badge&logo=docker&logoColor=white)

![Anaconda](https://img.shields.io/badge/Anaconda-%2344A833.svg?style=for-the-badge&logo=anaconda&logoColor=white)

![Linux](https://img.shields.io/badge/Linux-FCC624?style=for-the-badge&logo=linux&logoColor=white)

![AWS](https://img.shields.io/badge/AWS-%23FF9900.svg?style=for-the-badge&logo=amazon-aws&logoColor=white)

![Git](https://img.shields.io/badge/git-%23F05033.svg?style=for-the-badge&logo=git&logoColor=white)

## Project Structure

The project has been structured with the following folders and files:

- `data:` raw and clean data

- `src:` source code. It is divided into:

    - Notebooks with EDA, Baseline Model and AWS Pipelines incl. unit testing

    - `code_scripts`: processing, training, evaluation, docker container, serving and lambda

- `requirements.txt:` project requirements

## Project Description

The dataset was obtained from Kaggle and contains 5110  rows and 10 columns to detect stroke predictions. To prepare the data for modelling, an **Exploratory Data Analysis** was conducted where it was detected that the dataset is very imbalance (95% no stroke, 5% stroke). For modeling, the categorical features where encoded, XGBoost was use das model and the best roc-auc threshold was selected for the predictions using aditionally threshold-moving for the predictions due to the imbalance. The learning rate was tuned in order to find the best one on the deployed model.



    



## Pipeline Deployment

All pipelines where deployed on AWS SageMaker, as well as the Model Registry and Endpoints. The following pipelines where created:

- ✅ Preprocessing

- ✅ Training

- ✅ Tuning

- ✅ Evaluation

- ✅ Model Registry

- ✅ Model Conditional Registry

- ✅ Deployment

Additionally the experiments were tracked on Comel ML.