Study of 4,000 Experiments Finds Simple Retraining Schedules Beat Smarter ML Methods

A study by researcher Sawan Dasari ran nearly 4,000 experiments to evaluate how and when machine learning teams should retrain deployed models experiencing concept drift. The research compared four retraining strategies — no retraining, fixed periodic schedules, error-threshold triggers, and statistical drift detection — and found that for models lacking incremental learning, the choice of policy can swing accuracy by 15 to 55 percentage points. Counterintuitively, simple periodic retraining outperformed more sophisticated reactive approaches under both abrupt and gradual data drift. The study also identified a latency-budget interaction where compute constraints and retraining time compound together, potentially cutting a team's effective retraining capacity by half without them realizing it. The findings suggest most production ML teams are over-engineering their retraining triggers while underestimating the value of predictable, scheduled updates.