🩺 Diabetes Prediction Model – Your First MLOps Project (FastAPI + Docker + K8s)

🎥 YouTube video for the project: "Build Your First MLOps Project"

This project helps you learn Building and Deploying an ML Model using a simple and real-world use case: predicting whether a person is diabetic based on health metrics. We’ll go from:

• ✅ Model Training
• ✅ Building the Model locally
• ✅ API Deployment with FastAPI
• ✅ Dockerization
• ✅ Kubernetes Deployment

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📊 Problem Statement

Predict if a person is diabetic based on:

• Pregnancies
• Glucose
• Blood Pressure
• BMI
• Age

We use a Random Forest Classifier trained on the Pima Indians Diabetes Dataset.

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🚀 Quick Start

1. Clone the Repo

git clone https://github.com/iam-veeramalla/first-mlops-project.git
cd first-mlops-project

2. Create Virtual Environment

python3 -m venv .mlops
source .mlops/bin/activate

3. Install Dependencies

pip install -r requirements.txt

Train the Model

python train.py

Run the API Locally

uvicorn main:app --reload

Sample Input for /predict

{
  "Pregnancies": 2,
  "Glucose": 130,
  "BloodPressure": 70,
  "BMI": 28.5,
  "Age": 45
}

Dockerize the API

Build the Docker Image

docker build -t diabetes-prediction-model .

Run the Container

docker run -p 8000:8000 diabetes-prediction-model

Deploy to Kubernetes

kubectl apply -f diabetes-prediction-model-deployment.yaml

🙌 Credits

Created by ABHISHEK VEERAMALLA

Subscribe for more DevOps + MLOps content on the YouTube Channel - Abhishek.Veeramalla