Machine Learning Model Using Docker

Overview of the Project

The project is a basic application of Machine Learning using Docker for its deployment. It uses the Iris dataset and implements a Decision Tree Classifier that predicts the species of an iris flower from its features. In this application, Flask a lightweight web framework used so that users can pass input to the model using an API endpoint.

Instructions to Build and Run the Docker Container

1. Clone the Repository:

   • git clone https://github.com/shubhambhavsar/ML_Using_Docker.git
   • cd ML_Using_Docker

2. Build the Docker Image: Make sure that you are in the project directory and run the following command:

   • sudo docker build -t ml-app .

3. Run the Docker Container: Use following command to run the container:

   • sudo docker run -p 4000:80 ml-app

Instructions to Test the ML Endpoint

1. Access the Application:

   • Open your browser and navigate to http://localhost:4000 to see the running application.
   • Note: Use external IP address of the VM, if you are using any cloud services.

2. Test the /predict Endpoint: Test the /predict endpoint using curl or Postman by sending a POST request with JSON data:

   • curl -X POST http://localhost:4000/predict -H "Content-Type: application/json" -d '{"input": [5.1, 3.5, 1.4, 0.2]}'
   • Replace the input values with the features of the iris flower you want to predict.

Other Relevant Information

• Please ensure that the Docker is installed and running on your machine before you begin building or running the application.
• Install the requirements.txt dependencies.
• The code has a simple script train_model.py, which trains the Decision Tree Classifier and saves it as model.pkl.