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Building ML model serving APIs with Flask, request handling, and deployment.
Flask is a lightweight web framework perfect for serving ML models via HTTP endpoints. It's easy to learn and deploy.
Why Flask for ML?
Advantages:
- Simple: Minimal boilerplate, easy to learn
- Flexible: Highly customizable
- Lightweight: Minimal dependencies
- Testing: Easy to test
- Mature: Well-established, many tutorials
When to use:
- Simple to moderate complexity models
- Learning/prototyping
- Microservices
- When you need full control
When NOT to use:
- High-traffic APIs (use FastAPI, async)
- Complex request pipelines
- Need automatic documentation
Basic ML API with Flask
Example
from flask import Flask, request, jsonify
import pickle
import numpy as np
import logging
app = Flask(__name__)
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
# Load model at startup
try:
with open('model.pkl', 'rb') as f:
model = pickle.load(f)
logger.info("Model loaded successfully")
except Exception as e:
logger.error(f"Failed to load model: {e}")
model = None
@app.route('/health', methods=['GET'])
def health():
"""Health check endpoint"""
return jsonify({'status': 'healthy', 'model_loaded': model is not None}), 200
@app.route('/predict', methods=['POST'])
def predict():
"""Single prediction endpoint"""
try:
if not model:
return jsonify({'error': 'Model not loaded'}), 500
data = request.get_json()
if not data or 'features' not in data:
return jsonify({'error': 'No features provided'}), 400
# Convert to array
features = np.array(data['features']).reshape(1, -1)
# Predict
prediction = model.predict(features)[0]
return jsonify({
'prediction': float(prediction),
'status': 'success'
}), 200
except Exception as e:
logger.error(f"Prediction error: {str(e)}")
return jsonify({'error': str(e)}), 500
@app.route('/batch_predict', methods=['POST'])
def batch_predict():
"""Batch prediction endpoint"""
try:
data = request.get_json()
features = np.array(data['features'])
predictions = model.predict(features)
return jsonify({
'predictions': predictions.tolist(),
'count': len(predictions)
}), 200
except Exception as e:
return jsonify({'error': str(e)}), 500
@app.route('/model_info', methods=['GET'])
def model_info():
"""Get model information"""
return jsonify({
'model_type': str(type(model).__name__),
'n_features': getattr(model, 'n_features_in_', 'unknown'),
'version': '1.0'
}), 200
if __name__ == '__main__':
# Development server
app.run(debug=False, host='0.0.0.0', port=8000)
Testing Flask API
Example
import pytest
from main import app
@pytest.fixture
def client():
"""Create test client"""
app.config['TESTING'] = True
with app.test_client() as client:
yield client
class TestMLAPI:
def test_health(self, client):
"""Test health check endpoint"""
response = client.get('/health')
assert response.status_code == 200
assert response.json['status'] == 'healthy'
def test_predict_valid(self, client):
"""Test prediction with valid input"""
response = client.post('/predict', json={
'features': [1.0, 2.0, 3.0, 4.0]
})
assert response.status_code == 200
assert 'prediction' in response.json
assert 'status' in response.json
def test_predict_missing_features(self, client):
"""Test prediction with missing features"""
response = client.post('/predict', json={})
assert response.status_code == 400
assert 'error' in response.json
def test_batch_predict(self, client):
"""Test batch prediction"""
response = client.post('/batch_predict', json={
'features': [
[1.0, 2.0, 3.0],
[4.0, 5.0, 6.0],
[7.0, 8.0, 9.0]
]
})
assert response.status_code == 200
assert response.json['count'] == 3
def test_model_info(self, client):
"""Test model info endpoint"""
response = client.get('/model_info')
assert response.status_code == 200
assert 'model_type' in response.json
# Run tests: pytest test_api.py
Error Handling & Validation
Example
from flask import Flask, request, jsonify
from functools import wraps
app = Flask(__name__)
def validate_input(required_fields):
"""Decorator for input validation"""
def decorator(f):
@wraps(f)
def decorated_function(*args, **kwargs):
data = request.get_json()
if not data:
return jsonify({'error': 'Empty request body'}), 400
for field in required_fields:
if field not in data:
return jsonify({'error': f'Missing field: {field}'}), 400
return f(*args, **kwargs)
return decorated_function
return decorator
@app.route('/predict', methods=['POST'])
@validate_input(['features'])
def predict():
data = request.get_json()
# features guaranteed to exist here
return jsonify({'prediction': 1.0})
@app.errorhandler(400)
def bad_request(error):
return jsonify({'error': 'Bad request', 'details': str(error)}), 400
@app.errorhandler(500)
def internal_error(error):
return jsonify({'error': 'Internal server error'}), 500
Production Deployment with Gunicorn
bash example
# Install gunicorn
pip install gunicorn
# Basic deployment (4 workers)
gunicorn -w 4 -b 0.0.0.0:8000 main:app
# With logging
gunicorn -w 4 \
-b 0.0.0.0:8000 \
--log-level info \
--access-logfile - \
--error-logfile - \
main:app
# With timeout and restart settings
gunicorn -w 4 \
-b 0.0.0.0:8000 \
--timeout 60 \
--max-requests 1000 \
--max-requests-jitter 100 \
main:appDocker Deployment
Example
FROM python:3.9-slim
WORKDIR /app
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt
COPY . .
EXPOSE 8000
CMD ["gunicorn", "-w", "4", "-b", "0.0.0.0:8000", "main:app"]
bash example
docker build -t ml-api:1.0 .
docker run -p 8000:8000 ml-api:1.0API Client Example
Example
import requests
API_URL = 'http://localhost:8000'
# Health check
response = requests.get(f'{API_URL}/health')
print(response.json())
# Single prediction
response = requests.post(f'{API_URL}/predict', json={
'features': [1.0, 2.0, 3.0, 4.0]
})
print(f"Prediction: {response.json()['prediction']}")
# Batch prediction
response = requests.post(f'{API_URL}/batch_predict', json={
'features': [
[1.0, 2.0, 3.0],
[4.0, 5.0, 6.0]
]
})
print(f"Predictions: {response.json()['predictions']}")
Comparison: Flask vs FastAPI
| Feature | Flask | FastAPI |
|---|---|---|
| Learning Curve | Easy | Moderate |
| Performance | Good | Excellent |
| Async Support | Limited | Native |
| Documentation | Manual | Automatic |
| Type Hints | Optional | Required |
| Deployment | Gunicorn | Uvicorn/Gunicorn |
Choose Flask for:
- Simple models
- Learning
- Moderate traffic
Choose FastAPI for:
- High-traffic APIs
- Complex features
- Auto documentation needed
Summary
Flask for ML:
- Simple to implement
- Easy to test
- Lightweight
- Good for learning
- Production-ready with gunicorn
Exam Focus
Revise definitions, diagrams, examples, and short-answer points for Flask API for Machine Learning.
Interview Use
Prepare one clear explanation, one practical example, and one common mistake for this Machine Learning topic.
Search Terms
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Related Machine Learning Topics
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MlopsCI/CD for Machine LearningImplementing continuous integration and continuous deployment pipelines for ML models including testing, validation, and automated deployment.MlopsDocker for Machine LearningContainerizing ML models and workflows using Docker for reproducibility, scalability, and deployment.MlopsFastAPI for Machine Learning APIsBuilding high-performance ML APIs with FastAPI, async support, and automatic documentation.MlopsMLOps: IntroductionIntroduction to MLOps: productionizing ML systems, monitoring, deployment, and lifecycle management.MlopsModel Deployment StrategiesDifferent approaches to deploying ML models: batch, real-time, shadow, canary, blue-green, and A/B testing.MlopsML Model Monitoring & ObservabilityMonitoring ML models in production: performance metrics, data drift detection, alerting, and logging.