ModelOps is the practice of managing the full lifecycle of AI models — from training and testing through deployment, monitoring, and eventual retirement. It ensures models remain secure, updated, and aligned with business needs over time.
As organizations run more models across more applications, managing that complexity without dedicated processes and tooling leads to outdated models running in production, unknown failure modes, and no accountability for model behavior. ModelOps provides the structure to prevent that from happening.
