Model Lifecycle Management
Model lifecycle management refers to the end-to-end process of managing machine learning and artificial intelligence (AI) models from initial development through deployment, monitoring, maintenance, and eventual retirement. It includes stages such as data preparation, model training, validation, versioning, deployment, performance tracking and retraining. Effective life cycle management ensures models remain accurate, secure, compliant, and aligned with business goals over time. It enables continuous improvement, reduces operational risk and supports scalability – making it a critical component of enterprise AI and machine learning operations practices.
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