Model Deployment & Serving (Flask & FastAPI)

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Machine Learning Tutorial Part 16: <!DOCTYPE html> <html lang="en"> <head> <meta charset="UTF-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0"/> <meta name="description" content="Learn how to deploy machine learning models using Flask and FastAPI with real-world examples. Serve predictions with RESTful APIs." /> <meta name="keywords" content="Model Deployment, Flask API, FastAPI, Serve ML Models, Machine Learning Production, DarchumsTech" /> <meta name="author" content="DarchumsTech" /> <title>Machine Learning Tutorial Part 16: Model Deployment & Serving (Flask & FastAPI)

Machine Learning Tutorial Part 16: Model Deployment & Serving

🚀 Why Deploy ML Models?

After training an ML model, you need to make it accessible to real users. Deployment allows users or systems to send input data and receive predictions via an API.

🌐 Option 1: Flask API for Deployment

Flask is a lightweight Python web framework that can easily serve models as REST APIs.

from flask import Flask, request, jsonify
import pickle

model = pickle.load(open("model.pkl", "rb"))

app = Flask(__name__)

@app.route('/predict', methods=['POST'])
def predict():
    data = request.get_json()
    prediction = model.predict([data['features']])
    return jsonify({'prediction': prediction.tolist()})

if __name__ == '__main__':
    app.run(debug=True)

⚡ Option 2: FastAPI for Better Performance

FastAPI is a modern, high-performance framework that supports asynchronous code and automatic docs via Swagger UI.

from fastapi import FastAPI
from pydantic import BaseModel
import pickle

model = pickle.load(open("model.pkl", "rb"))

app = FastAPI()

class InputData(BaseModel):
    features: list

@app.post("/predict")
def predict(data: InputData):
    prediction = model.predict([data.features])
    return {"prediction": prediction.tolist()}

🧪 Test Your API Locally

You can use tools like Postman or curl to test your API:

curl -X POST http://localhost:8000/predict \
  -H "Content-Type: application/json" \
  -d '{"features": [5.1, 3.5, 1.4, 0.2]}'

🧑‍💻 Deployment Options

  • Local Server – for development/testing
  • Heroku – simple and free to start
  • Render / Railway – easy modern alternatives to Heroku
  • AWS/GCP/Azure – production-level hosting with scalability

📝 Quiz 1: Deployment Basics

Q: Which Python library is known for automatic API docs and speed?

A: FastAPI.

📝 Quiz 2: Model Serving

Q: Which HTTP method should you use to send prediction data to your API?

A: POST method — used to send data in the request body for processing.

📌 Summary

  • Flask and FastAPI are great tools for serving ML models.
  • Use pickle or joblib to save/load models.
  • Always test your API locally before deploying.

Machine Learning Tutorial Part 16: Model Deployment & Serving

🚀 Why Deploy ML Models?

After training an ML model, you need to make it accessible to real users. Deployment allows users or systems to send input data and receive predictions via an API.

🌐 Option 1: Flask API for Deployment

Flask is a lightweight Python web framework that can easily serve models as REST APIs.

from flask import Flask, request, jsonify
import pickle

model = pickle.load(open("model.pkl", "rb"))

app = Flask(__name__)

@app.route('/predict', methods=['POST'])
def predict():
    data = request.get_json()
    prediction = model.predict([data['features']])
    return jsonify({'prediction': prediction.tolist()})

if __name__ == '__main__':
    app.run(debug=True)

⚡ Option 2: FastAPI for Better Performance

FastAPI is a modern, high-performance framework that supports asynchronous code and automatic docs via Swagger UI.

from fastapi import FastAPI
from pydantic import BaseModel
import pickle

model = pickle.load(open("model.pkl", "rb"))

app = FastAPI()

class InputData(BaseModel):
    features: list

@app.post("/predict")
def predict(data: InputData):
    prediction = model.predict([data.features])
    return {"prediction": prediction.tolist()}

🧪 Test Your API Locally

You can use tools like Postman or curl to test your API:

curl -X POST http://localhost:8000/predict \
  -H "Content-Type: application/json" \
  -d '{"features": [5.1, 3.5, 1.4, 0.2]}'

🧑‍💻 Deployment Options

  • Local Server – for development/testing
  • Heroku – simple and free to start
  • Render / Railway – easy modern alternatives to Heroku
  • AWS/GCP/Azure – production-level hosting with scalability

📝 Quiz 1: Deployment Basics

Q: Which Python library is known for automatic API docs and speed?

A: FastAPI.

📝 Quiz 2: Model Serving

Q: Which HTTP method should you use to send prediction data to your API?

A: POST method — used to send data in the request body for processing.

📌 Summary

  • Flask and FastAPI are great tools for serving ML models.
  • Use pickle or joblib to save/load models.
  • Always test your API locally before deploying.

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