AI Production Deployment Checklist: From Notebook to Live System

Deploying an AI model is not a single event. It is a series of steps: packaging, serving, monitoring, alerting and maintaining. This checklist covers every step.

We follow this checklist in our Applied AI Engineering service, ensuring every deployment is reliable.

Packaging

Package the model with its dependencies, configuration and preprocessing code. Use containers for consistency. Document the package so it can be reproduced.

Serving

Serve the model through a reliable API. Handle load, errors and timeouts gracefully. Use caching for common predictions. Monitor latency and throughput.

Monitoring

Monitor prediction accuracy, input distribution, latency and error rates. Set alerts for thresholds. A model that is not monitored is a model that will fail silently.

Alerting

Alert on accuracy drops, latency spikes, error rate increases and input anomalies. Alerts should be actionable, not noisy. Every alert should trigger a response.

Maintaining

Retrain the model regularly with fresh data. Version every model and every dataset. Roll back to a previous version if the new model degrades. Document every change.

Key takeaways

Frequently asked questions

What is applied AI engineering?

Applied AI engineering is the practice of building real-world AI systems that solve specific business problems. It covers model selection, training, deployment, integration and maintenance.

Where is TPR Media based?

TPR Media operates from Level 34, 1 Eagle Street, Brisbane City QLD 4000, serving clients across Brisbane and Australia-wide.

TPR Media provides an AI production deployment checklist: packaging, serving, monitoring, alerting and maintaining for reliable live systems.