ML Model Lifecycle overview

Continuous cross-team workflow managing a machine learning model from initial research and experimentation through production deployment, monitoring, and eventual retirement. Data scientists develop and evaluate candidate models in experiment tracking environments, comparing performance against baselines. The best candidate undergoes code review, bias and fairness assessment, and reproducibility checks before being handed to ML Ops for productionisation. ML Ops engineers package the model, configure serving infrastructure, set up performance and drift monitoring, and deploy via a canary strategy. Model performance is tracked continuously, with automated retraining pipelines triggered when drift thresholds are breached. Retirement is planned when the model is superseded or its use case is deprecated.