Continuous Training and Automated Retraining

Continuous training (CT) automates repeated candidate creation as data or schedules change. A pipeline can run at a fixed cadence, respond to a new labeled dataset or trigger after a monitored signal. The pipeline typically validates inputs, assembles a training set, trains a candidate, evaluates it against baselines and release criteria, stores artifacts and may promote a passing candidate. Automation reduces repetitive steps but does not make model selection decisions scientifically sound by itself. Scheduled retraining is predictable and easier to budget, but may train when little new information has arrived. Event-triggered retraining can respond quickly to new labels or drift alerts, but triggers can be noisy or caused by broken ingestion. A useful design validates trigger evidence before spending compute, applies minimum data-volume and freshness requirements, and includes safeguards against repeated runs. Every candidate should be evaluated using a process appropriate to deployment: time-aware splits for temporal tasks, grouped splits where entities repeat, relevant subgroup analysis and comparison to the currently deployed model. Metrics and thresholds should be defined in advance. A candidate that performs better on one aggregate measure may have worse calibration, latency, fairness or operational stability. Keep human approval for high-impact releases or uncertain results. Automated training requires reproducible lineage: data version and time window, source commit, dependency environment, parameters, random seeds and artifact identity. Store the evaluation report alongside the exact artifact digest. Promotion should be a separate controlled step with a canary or staged rollout and a tested rollback path. Monitor post-deployment behavior and retain the previous model. Continuous training is distinct from continuous deployment: a new model can be generated and evaluated without automatically shipping it. The pipeline should fail closed when data checks, evaluation or artifact verification fails, while preserving logs that make the failure diagnosable.