ML model deployment bridges the gap between experimentation and production value. Models require versioning, performance monitoring, and infrastructure optimized for inference. Multiple deployment patterns suit different requirements.
Deployment Patterns
Batch inference processes data periodically for offline predictions. Real-time serving provides immediate predictions via APIs. Edge deployment runs models locally on devices. Each pattern has different infrastructure requirements.
- Use batch inference for periodic predictions not requiring real-time
- Deploy REST/gRPC APIs for real-time prediction serving
- Consider edge deployment for latency-sensitive or offline scenarios
- Implement model registries tracking versions and metadata
- Canary deployments enable safe model updates
Monitoring and Maintenance
Monitor prediction latency and throughput. Track data drift indicating model degradation. Compare predictions to ground truth when available. Plan for model retraining and updates.