Model Deployment Guide 🚀🤖

Bring AI Models to Real World

1. What is Model Deployment?

Model Deployment is the process of making a trained machine learning model available for real-world use via apps, APIs, or websites.

2. Steps

1. Train model
2. Save model
3. Build API
4. Connect frontend
5. Deploy online

3. Save Model

import joblib

joblib.dump(model, "model.pkl")

4. Load Model

model = joblib.load("model.pkl")

5. Build API (Flask)

from flask import Flask, request, jsonify
import joblib

app = Flask(__name__)
model = joblib.load("model.pkl")

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

app.run()

6. Connect Frontend

fetch("http://localhost:5000/predict", {
  method: "POST",
  headers: { "Content-Type": "application/json" },
  body: JSON.stringify({ input: [5] })
})

7. Deployment Platforms

- Vercel (frontend)
- Render / Railway (backend)
- AWS / GCP (advanced)

8. Real Use Cases