DevOps • 60 mins

Model Deployment

Guide to deploying machine learning models to production environments with DevOps and MLOps best practices

What You'll Learn

Understanding model deployment requirements

Containerization of ML models

CI/CD pipelines for ML workflows

Monitoring and observability strategies

Scaling and load balancing techniques

MLOps architecture and best practices

Nim Hewage

Co-founder & AI Strategy Consultant

Over 13 years of experience implementing AI solutions across Global Fortune 500 companies and startups. Specializes in enterprise-scale AI transformation, MLOps architecture, and AI governance frameworks.

View All Tutorials

Model Deployment Approaches

Web Services

API Deployment

Deploying machine learning models as RESTful APIs for applications to consume

Implementation Steps

1Package model and dependencies
2Create API endpoints to handle predictions
3Implement input validation and preprocessing
4Set up request/response serialization
5Configure authentication and rate limiting
6Deploy API with horizontal scaling capabilities

Recommended Tools

FlaskFastAPIDjango RESTTensorFlow ServingSwagger/OpenAPI

Code Example

# Example of Flask API deployment for ML model
import pickle
import numpy as np
from flask import Flask, request, jsonify

app = Flask(__name__)

# Load the pre-trained model
with open('model.pkl', 'rb') as f:
    model = pickle.load(f)

@app.route('/predict', methods=['POST'])
def predict():
    # Get data from request
    data = request.json
    
    # Validate input
    if not data or 'features' not in data:
        return jsonify({'error': 'No features provided'}), 400
    
    # Preprocess input
    try:
        features = np.array(data['features']).reshape(1, -1)
    except Exception as e:
        return jsonify({'error': f'Invalid features format: {str(e)}'}), 400
    
    # Make prediction
    try:
        prediction = model.predict(features).tolist()
        if hasattr(model, 'predict_proba'):
            probabilities = model.predict_proba(features).tolist()
            response = {
                'prediction': prediction[0],
                'probabilities': probabilities[0]
            }
        else:
            response = {'prediction': prediction[0]}
        
        return jsonify(response)
    except Exception as e:
        return jsonify({'error': f'Prediction error: {str(e)}'}), 500

# Health check endpoint
@app.route('/health', methods=['GET'])
def health():
    return jsonify({'status': 'healthy'})

if __name__ == '__main__':
    app.run(host='0.0.0.0', port=8000)