Ai Model Deployment — Free AI & ML Tutorial

TL;DR: Learn Ai Model Deployment in AI & ML with a free, beginner-friendly tutorial, examples and practice for Indian students on Syllab.in.

Ai Model Deployment in AI & ML

Training an ML model is only 10% of the work. Getting it into production — deployed as a web service that users can call — is 90% of the real work.

Model serving: Host trained model as a REST API. Users send input → API calls model → returns prediction. Flask is a lightweight Python framework for this.

Deployment platforms: AWS Lambda, Google Cloud Run, Heroku, Docker containers, or managed platforms like AWS SageMaker.

Key considerations: model versioning, A/B testing (serve two versions, measure which is better), monitoring (is model still accurate on real data?), latency (how fast is prediction?).

# Flask REST API (minimal):
# from flask import Flask, request
# app = Flask(__name__)
#
# @app.route('/predict', methods=['POST'])
# def predict():
#     data = request.json
#     result = model.predict(data)
#     return {'prediction': result}
#
# if __name__ == '__main__':
#     app.run()

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