Deploying Machine Learning Models with Flask: A Step-by-Step Guide

Deploying machine learning models involves making your model accessible for others to use, typically through a web interface or an API. Flask is a lightweight web framework in Python that is commonly used for deploying machine learning models. Below is a step-by-step guide to deploying a machine learning model with Flask:

1. Train and Save the Model:

• Train your machine learning model using a library like scikit-learn or TensorFlow.
• Save the trained model to a file using a serialization library (e.g., joblib for scikit-learn models or TensorFlow's SavedModel for TensorFlow models).

!pip install Flask

2. Create a Flask App:

• Install Flask using:
• Create a new directory for your project and navigate into it.
• Inside the directory, create a file named app.py (or another name of your choice).

from flask import Flask, request, render_template
import joblib  # or any other library for loading your model

3. Set Up Flask App Structure:

• Import necessary modules:

from flask import Flask, request, render_template…