Machine Learning Operations - Diabetes

Serverless machine learning batch prediction on AWS.

📖 Project

👨🏻‍🏫 Introduction

The focus of this project is on the productionizing/operation of a serverless Machine Learning model on AWS.

A simple dataset was chosen for this task (US National Institute of Diabetes, Digestive and Kidney Diseases: Pima Indian Diabetes). The dataset presents diagnostic measurements from female patients of at least 21 years old of Pima heritage as variables and indicates if the patient has or does not have diabetes.

A quick process of ML is developed in the notebook of the project. Data is ingested from AWS S3, the model - that predicts if a patient has diabetes or not based on 8 medical exam features - is created locally and deployed using serverless framework to AWS Lambda. Then, AWS API Gateway makes it available for the public.

You can get an example prediction for a 55 years-old patient, who had 3 pregnancies, with a glucose of 200,a blood-pressure of 100, an insulin of 200, triceps skin thickness of 50 an a diabetes genetic function of 0.5 in the following link:

https://q6l78vh36a.execute-api.sa-east-1.amazonaws.com/development/lambda-prediction?pregnancies=3&glucose=200&blood_pressure=100&skin_thickness=50&insulin=200&bmi=40&diabetes_pedigree_function=0.5&age=55

(You may change the parameters directly on the URL to get different predictions)

🎯 Goal

Deploy serverless Machine Learning predictions on AWS.

📊 Results

The model achieved an accuracy of 72% and an ROC-AUC of 71%.

📈 Features

Feature	Description
pregnancies	Number of pregnancies the patient had.
glucose	Plasma glucose concentration 2 hours after an oral glucose tolerance test.
blood_pressure	Diastolic blood pressure (mm Hg).
skin_thickness	Triceps skin fold thickness (mm).
insulin	2-Hour serum insulin exam (mu U/ml).
bmi	Body mass index (weight in kg/(height in m)^2).
diabetes_pedigree_function	Genetic predisposition to diabetes (from 0 to 1).
age	Patient age.

🗺 Methodology

🗄 Notebooks

• 1.0-creating-the-model.ipynb

📦 Folder Structure

├── LICENSE
├── Makefile           <- Makefile with commands like `make deploy_model`
├── README.md          <- The top-level README for developers using this project.
├── data
│
├── models             <- Trained and serialized models.
│
├── notebooks          <- Jupyter notebooks. Naming convention is a number (for ordering),
│                         and a short `-` delimited description, e.g.
│                         `1.0--creating-the-model.ipynbß`.
│
├── deployment         <- All files dockerized and deployed to AWS Lambda, like "handler.py".
│
├── references         <- Explanatory materials.
│
├── requirements.txt   <- The requirements file for reproducing the analysis environment, e.g.
│                         generated with `pip list --format=freeze > requirements.txt`