In Python machine learning, how do I expose a REST API?

In Python machine learning, exposing a REST API allows you to serve your ML models in a way that they can be accessed over the web. This can be done using various frameworks, but Flask is one of the popular choices due to its simplicity and ease of use.

To get started, you can follow this basic example where we create a REST API to serve predictions from a simple machine learning model.

from flask import Flask, request, jsonify import joblib app = Flask(__name__) # Load your trained ML model model = joblib.load('your_model.pkl') @app.route('/predict', methods=['POST']) def predict(): data = request.get_json(force=True) prediction = model.predict([data['input']]) return jsonify({'prediction': prediction.tolist()}) if __name__ == '__main__': app.run(debug=True)

Python machine learning REST API Flask serve models web access

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