1. Introduction

Goal of the Tutorial

This tutorial aims to guide you on how to deploy a machine learning model using Flask, a micro web framework written in Python. By the end of this tutorial, you will have a machine learning model running on a Flask server that can be accessed via HTTP requests.

What You Will Learn

You will learn the following:

• Basics of Flask and how to set up a Flask server
• How to load a machine learning model into your Flask app
• How to create API endpoints that can use the machine learning model
• How to deploy your Flask app

Prerequisites

Before starting this tutorial, you should have:

• Basic understanding of Python
• Basic understanding of Machine Learning
• A Python environment set up on your computer. If not, install Python and pip

2. Step-by-Step Guide

Setting Up Flask Environment

First, install Flask using pip:

pip install Flask

Creating a Flask App

In a new Python file, import Flask and create an app instance:

from flask import Flask

app = Flask(__name__)

Loading a Machine Learning Model

Assuming you have a trained model saved as model.pkl, you can load it using joblib:

from joblib import load

model = load('model.pkl')

Creating API Endpoints

Next, let's create an endpoint that uses the model to predict. We'll use the @app.route decorator to specify the URL and request to get the data sent to the API:

from flask import request

@app.route('/predict', methods=['POST'])
def predict():
    data = request.json
    prediction = model.predict(data)
    return {'prediction': prediction.tolist()}

Running the Flask App

Finally, run the Flask app:

if __name__ == '__main__':
    app.run(debug=True)

3. Code Examples

Full Code Example

Here is the full code of our Flask app:

from flask import Flask, request
from joblib import load

app = Flask(__name__)
model = load('model.pkl')

@app.route('/predict', methods=['POST'])
def predict():
    data = request.json
    prediction = model.predict(data)
    return {'prediction': prediction.tolist()}

if __name__ == '__main__':
    app.run(debug=True)

4. Summary

In this tutorial, we've covered:

• How to set up a Flask server
• How to load a machine learning model into a Flask app
• How to create API endpoints that use the machine learning model

Next, you may want to learn how to deploy your Flask app to a server so it can be accessed from anywhere. Check out the Flask documentation for more information.

5. Practice Exercises

Exercise 1

Create a Flask app that loads a different machine learning model and creates an endpoint for predicting.

Exercise 2

Modify the /predict endpoint to accept GET requests and get data from the query string.

Exercise 3

Add error handling to the /predict endpoint to return a useful message if the prediction fails.

Remember, the key to learning programming is practice!