There’s a common belief that fresher data yields better results, but how frequently should you update your models?
To figure this out, train models on different past timeframes and test them on current data.
For instance, train model A on January-May data, model B on April-August, and model C on July-November, then evaluate all on December data.
If model A performs much worse than model C, you should consider updating your model more frequently to maintain high performance.
Reference: Designing Machine Learning Systems by Chip Huyen.
