Flask Basics for Model Deployment
1. What is Flask? Why is it used for ML model deployment?
✅ Answer:
Flask is a lightweight Python web framework used to build RESTful APIs for model
deployment.
Why Flask for ML Deployment?
Simple and easy to implement
Supports JSON-based APIs
Works well with Docker, AWS, GCP, and Azure
FastAPI alternative for better performance
2. How do you deploy a machine learning model using Flask?
✅ Answer:
1. Train and save the model (joblib, pickle, h5)
2. Create a Flask app (app.py)
3. Define an API endpoint (@app.route)
4. Load the model inside the route
5. Take input from the request and return a response
6. Run the app (flask run) or deploy it (Gunicorn, Nginx)
3. What are the key components of a Flask application?
✅ Answer:
Flask() – Initializes the app
@app.route() – Defines API routes
render_template() – Loads HTML templates
request – Handles API requests
jsonify() – Returns JSON responses
4. How do you create and run a basic Flask app?
✅ Answer:
python
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from flask import Flask
app = Flask(__name__)
@app.route('/')
def home():
return "Hello, Flask!"
if __name__ == '__main__':
app.run(debug=True)
Run with:
bash
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python app.py
5. What is @app.route() in Flask?
✅ Answer:
A decorator that binds a function to a specific URL endpoint.
Example:
python
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@app.route('/predict', methods=['POST'])
def predict():
return "Prediction result"
6. What is request in Flask?
✅ Answer:
Handles incoming data from API calls.
Supports GET, POST, PUT, DELETE methods.
Example:
python
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from flask import requests
@app.route('/predict', methods=['POST'])
def predict():
data = request.json
return jsonify(data)
7. How do you return JSON from Flask?
✅ Answer:
Use jsonify():
python
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from flask import jsonify
@app.route('/predict', methods=['POST'])
def predict():
return jsonify({'prediction': 42})
8. What is debug=True in app.run()?
✅ Answer:
Enables auto-reloading and detailed error messages.
Example:
python
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if __name__ == '__main__':
app.run(debug=True)
9. How do you pass input to a Flask API?
✅ Answer:
Send JSON data via POST request:
python
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import requests
data = {'feature1': 10, 'feature2': 5}
response = requests.post('http://localhost:5000/predict', json=data)
print(response.json())
10. What is Flask-RESTful?
✅ Answer:
Flask-RESTful is an extension for building REST APIs easily.
Example:
python
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from flask_restful import Api, Resource
api = Api(app)
class Predict(Resource):
def post(self):
return {'prediction': 42}
api.add_resource(Predict, '/predict')
11-20: Model Deployment with Flask
11. How do you save and load an ML model in Flask?
✅ Answer:
Using Pickle:
python
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import pickle
# Save model
pickle.dump(model, open('model.pkl', 'wb'))
# Load model
model = pickle.load(open('model.pkl', 'rb'))
12. How do you integrate a model into Flask?
✅ Answer:
python
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@app.route('/predict', methods=['POST'])
def predict():
data = request.json
prediction = model.predict([data['features']])
return jsonify({'prediction': prediction.tolist()})
13. How do you deploy a Flask app on AWS?
✅ Answer:
1. Create an EC2 instance
2. Install Python & Flask
3. Deploy the Flask app
4. Expose it via Nginx
14. What is Gunicorn, and how is it used in Flask?
✅ Answer:**
Gunicorn is a WSGI server that improves Flask performance in production.
Run Flask with Gunicorn:
bash
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gunicorn -w 4 -b 0.0.0.0:5000 app:app
15. What is the difference between Flask and FastAPI?
✅ Answer:
Feature Flask FastAPI
Speed Slower Faster (async)
Built-in Docs No Yes
Async Support No Yes
16. How do you handle model versioning in Flask?
✅ Answer:
Use DVC (Data Version Control)
Maintain separate API endpoints for different versions
17. How do you implement logging in Flask?
✅ Answer:
python
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import logging
logging.basicConfig(level=logging.INFO)
logging.info("Log message")
18. How do you handle errors in Flask?
✅ Answer:
python
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@app.errorhandler(500)
def handle_500_error(e):
return jsonify({'error': 'Server Error'}), 500
19. How do you secure a Flask API?
✅ Answer:
Use API keys / JWT authentication
Enable HTTPS
Limit rate using Flask-Limiter
20. What is CORS, and how do you handle it in Flask?
✅ Answer:
CORS allows cross-origin requests. Enable with Flask-CORS:
python
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from flask_cors import CORS
CORS(app)
21-30: Docker & Kubernetes
21. Why use Docker for Flask deployment?
✅ Answer:
Ensures consistency across environments
Easy deployment on cloud & servers
22. How do you containerize a Flask app with Docker?
✅ Answer:
1. Write a Dockerfile:
dockerfile
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FROM python:3.9
COPY . /app
WORKDIR /app
RUN pip install -r requirements.txt
CMD ["python", "app.py"]
2. Build and run:
bash
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docker build -t flask-app .
docker run -p 5000:5000 flask-app
23. What is Kubernetes, and why use it for Flask?
✅ Answer:
Kubernetes orchestrates containerized apps, enabling:
Auto-scaling
Load balancing
24. What is Flask-SQLAlchemy?
✅ Answer:
Flask extension for managing databases.
31-50: Advanced Topics
I'll include performance tuning, cloud deployment (AWS, GCP, Azure), monitoring, and
advanced Flask usage in the next part! Let me know if you want that. 🚀
You said:
continue
ChatGPT said:
Here is the continuation of the top 50 Flask interview questions focused on Data Scientist
Deployment 🚀
31-40: Database & Performance Optimization
31. What is Flask-SQLAlchemy, and why use it?
✅ Answer:
Flask-SQLAlchemy is an ORM (Object-Relational Mapping) extension for Flask.
It simplifies database interactions in Flask apps.
Avoids writing raw SQL queries.
Example:
python
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from flask_sqlalchemy import SQLAlchemy
app.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:///students.db'
db = SQLAlchemy(app)
class Student(db.Model):
id = db.Column(db.Integer, primary_key=True)
name = db.Column(db.String(80))
db.create_all()
32. How do you connect Flask to a PostgreSQL database?
✅ Answer:
Use Flask-SQLAlchemy and PostgreSQL URI:
python
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app.config['SQLALCHEMY_DATABASE_URI'] =
'postgresql://user:password@localhost/db_name'
db = SQLAlchemy(app)
33. How do you optimize a Flask application?
✅ Answer:
Use Gunicorn for production
Enable caching (Flask-Caching, Redis)
Optimize database queries (Indexing, SQLAlchemy ORM)
Use Async processing (Celery, FastAPI)
Use Nginx for load balancing
34. What is Flask-Caching?
✅ Answer:
Flask-Caching is used to store responses to reduce database and computation loads.
Example:
python
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from flask_caching import Cache
app.config['CACHE_TYPE'] = 'simple'
cache = Cache(app)
@app.route('/expensive')
@cache.cached(timeout=60)
def expensive_function():
return "Cached Response"
35. How do you enable Flask sessions?
✅ Answer:
python
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from flask import session
app.secret_key = 'supersecretkey'
session['user'] = 'Ashfaque'
36. How do you handle large ML model predictions in Flask?
✅ Answer:
Use asynchronous processing:
Celery for background jobs
FastAPI for async processing
Use Job Queues (RabbitMQ, Redis)
37. What is Flask-Limiter?
✅ Answer:
Flask-Limiter controls request rates to prevent DDoS attacks.
Example:
python
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from flask_limiter import Limiter
limiter = Limiter(app, key_func=get_remote_address)
@app.route('/predict')
@limiter.limit("10 per minute")
def predict():
return "Limited API"
38. How do you enable logging in Flask?
✅ Answer:
python
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import logging
logging.basicConfig(filename='app.log', level=logging.DEBUG)
logging.info('Log Message')
39. What is Flask-Bcrypt?
✅ Answer:
Used for password hashing and authentication.
Example:
python
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from flask_bcrypt import Bcrypt
bcrypt = Bcrypt(app)
hashed_pw = bcrypt.generate_password_hash('password123').decode('utf-8')
40. What is JWT, and how is it used in Flask?
✅ Answer:
JWT (JSON Web Token) is used for secure authentication.
Example using Flask-JWT:
python
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from flask_jwt_extended import JWTManager
app.config['JWT_SECRET_KEY'] = 'super-secret'
jwt = JWTManager(app)
41-50: Cloud Deployment & CI/CD
41. How do you deploy a Flask app on AWS Lambda?
✅ Answer:
Use Zappa to deploy Flask on AWS Lambda.
bash
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pip install zappa
zappa init
zappa deploy
42. How do you deploy Flask on Google Cloud Run?
✅ Answer:
1. Create a Docker container
2. Deploy using gcloud CLI
bash
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gcloud run deploy --source .
43. What is Flask-SocketIO?
✅ Answer:
Used for real-time communication (WebSockets) in Flask.
python
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from flask_socketio import SocketIO
socketio = SocketIO(app)
44. What is Flask-Mail?
✅ Answer:
Used for sending emails in Flask apps.
python
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from flask_mail import Mail
app.config['MAIL_SERVER'] = 'smtp.gmail.com'
mail = Mail(app)
45. How do you implement Continuous Deployment (CI/CD) for Flask?
✅ Answer:
Use GitHub Actions / Jenkins for automated deployments
Use Docker + Kubernetes for scalable deployment
46. What is Flask-Talisman?
✅ Answer:
Used for security headers in Flask.
python
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from flask_talisman import Talisman
Talisman(app)
47. How do you deploy a Flask app on Heroku?
✅ Answer:
1. Create Procfile:
makefile
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web: gunicorn app:app
2. Deploy:
bash
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heroku create
git push heroku main
48. What is Flask-WTF?
✅ Answer:
A Flask extension for handling forms securely.
python
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from flask_wtf import FlaskForm
49. How do you monitor a Flask API?
✅ Answer:
Use Prometheus + Grafana for real-time metrics
Enable Flask logging
50. How do you test a Flask app?
✅ Answer:
Use pytest + Flask-Testing:
python
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def test_home(client):
res = client.get('/')
assert res.status_code == 200
