Machine Learning Deployment Strategies

Deploying machine learning models to production is often more challenging than training them. This guide covers proven deployment strategies.

Deployment Patterns

Common deployment patterns include:

• Batch Prediction: Process large volumes of data on a schedule
• Real-time API: Serve predictions via REST or gRPC endpoints
• Edge Deployment: Run models on edge devices for low-latency inference

Monitoring and Maintenance

Production models require continuous monitoring for performance degradation, data drift, and concept drift. Implement automated retraining pipelines.

A/B Testing

Use A/B testing to validate new model versions before full rollout. This reduces risk and provides data-driven confidence in model improvements.