Information about using Flask with machine learning. Flask is a lightweight web framework in Python that’s often used to build web applications, including those that host machine learning models.

Here’s a simple guide to deploying a machine learning model using Flask:

1. Create a Machine Learning Model: First, you’ll need to create and train a machine learning model using a library like Scikit-learn, TensorFlow, or PyTorch.
2. Export the Model: Once your model is trained, you can serialize it using a library like Pickle.
3. Create a Flask App: You can create a Flask application that loads your model and sets up routes to handle incoming requests.
4. pythonCopy code
5. from flask import Flask, request, jsonify
6. import pickle
7.  
8. app = Flask(__name__)
9.  
10. # Load the model
11. model = pickle.load(open(‘model.pkl’, ‘rb’))
12.  
13. @app.route(‘/predict’, methods=[‘POST’])
14. def predict():
15.     data = request.json
16.     prediction = model.predict([data[‘features’]])
17.     return jsonify({‘prediction’: prediction[0]})
18.  
19. if __name__ == ‘__main__’:
20.     app.run()
21. Send Requests to Your Model: Once your Flask app is running, you can send HTTP requests to it. The data for prediction can be sent as JSON in the request body.
22. Deploy: If you want to make your model accessible to others, you can deploy your Flask app to a web server or platform like AWS, Heroku, or Google Cloud.