For Heads-of · Practitioner
Model Deployment
The process of putting a trained AI model into production, where it makes real-world predictions or decisions that affect users or business outcomes.
- deployment
- production
- infrastructure
- operations
Considerations
Choice of infrastructure (cloud, on-premises or edge), API design, latency and throughput, security and access controls, and monitoring and rollback capabilities.
Challenges
Models often behave differently in production than in testing because of shifts in the data, inputs nobody anticipated, or deliberate attack. Deployment needs monitoring and a response plan from the first day.
Governance responsibility
The organisation is responsible for the system's behaviour in production. It needs to confirm the model performs acceptably before release, monitor it afterwards, and be able to withdraw or patch it quickly. Controls that exist at approval and lapse in operation are the AI system lifecycle controls gap; AI incident response and rollback is the withdrawal route.
Related Terms
A/B Testing
An experimental method that compares two versions of a system (A and B) by sending each to a random subset of users, to establish which performs better.
Incident Response
A structured process for detecting, assessing, containing and resolving adverse events involving AI systems, such as model failure, a security breach or a fairness harm.
Model Monitoring
Continuous observation of a deployed model's inputs, outputs, performance and behaviour, to detect degradation, drift, misuse or security anomalies.
Model Versioning
The practice of recording which model, trained how and on what, was deployed when, enabling comparison, rollback and reproducibility. For systems built on models the organisation did not train, it extends to versioning the surrounding assembly, since that is the only part it controls.