Iris Classification Microservice

The Iris dataset is a classic dataset in the field of machine learning and statistics. It contains 150 observations of iris flowers, each described by four features: sepal length, sepal width, petal length, and petal width. Each observation is labeled with one of three classes: Iris setosa, Iris versicolor, and Iris virginica. This dataset is widely used for classification tasks.

This project implements a microservice for classifying Iris flower species based on the famous Iris dataset. The microservice uses a machine learning model built with scikit-learn and is deployed using Docker and Google Cloud Run.

Dataset

The Iris dataset is a well-known dataset used for pattern recognition and classification. It includes 150 records, each containing 4 features: sepal length, sepal width, petal length, and petal width, along with the species of Iris flowers as the target variable.

Features

• Sepal Length
• Sepal Width
• Petal Length
• Petal Width

Target: The three Iris species classes: Setosa, Versicolor, and Virginica.

Project Structure

├── app.py # Flask app for serving predictions

├── iris_model.pkl # Pre-trained machine learning model

├── Dockerfile # Instructions for building Docker image

├── requirements.txt # Python dependencies

├── templates

│ ├── index.html # Frontend form for input

Local Development

Prerequisites

• Python 3.x
• Flask
• Docker

Step-by-step Instructions

1. Clone the repository:

   git clone https://github.com/yourusername/iris_classification_microservice.git
   cd iris_classification_microservice

2. Install dependencies: Install the necessary Python packages by running:

   pip install -r requirements.txt

3. Run the application locally: You can run the Flask app locally to test the microservice.

   python app.py

4. Access the application: Once the application is running, you can open it in your browser. You can input the values for Sepal Length, Sepal Width, Petal Length, and Petal Width to get a prediction of the Iris species.

Dockerization

This project uses Docker to containerize the Flask application.

Building the Docker Image

docker build -t iris_classification

This command builds the Docker image with the tag iris_classification based on the instructions in the Dockerfile.

Tagging the Image

docker tag iris_classification gcr.io/microservicezee/iris_classification

This command tags the local Docker image iris_classification for pushing to the Google Container Registry with the name gcr.io/microservicezee/iris_classification.

Pushing the Docker Image

docker push gcr.io/microservicezee/iris_classification

This command pushes the Docker image to the Google Container Registry (GCR) in the project microservicezee.

Deployment to Google Cloud Run

To deploy the microservice to Google Cloud Run, run the following command:

gcloud run deploy iris-classification-service --image gcr.io/microservicezee/iris_classification --platform managed --region us-central1 --allow-unauthenticated

This command deploys the Docker image to Google Cloud Run as a fully managed service. It will create a URL for the service that can be accessed from anywhere.

Usage

Once deployed, you can access the Iris Classification Microservice through the generated Google Cloud Run URL for me it is : https://iris-classification-service-tftq37eyta-uc.a.run.app/. Input the required features (Sepal Length, Sepal Width, Petal Length, Petal Width), and the model will predict the species of the Iris flower.

VS Code Setup

For local development, we used VS Code to build the model and microservice. The following steps were followed:

1. Developed the machine learning model in VS Code.
2. Tested locally using Flask on localhost.
3. Dockerized the project and pushed it to Google Cloud Platform for deployment.

Technologies Used

• Python: Core programming language used for model building and API development.
• Flask: Web framework for serving the machine learning model as an API.
• scikit-learn: For model building.
• Docker: For containerizing the application.
• Google Cloud Run: For deploying the containerized application.
• VS Code: For local development and testing.