How often will the Active Learning model rebuild while actively coding in the queue?

The Active Learning model is designed to improve its accuracy by continuously updating based on new data and context. It is essential for it to rebuild at regular intervals to incorporate the latest information and adapt its learning process. In this case, choosing 20 minutes as the rebuild interval is indicative of a balance between keeping the model up-to-date and allowing enough time for it to gather sufficient data for meaningful updates.

Opting for 20 minutes allows the model to respond relatively promptly to changes without overwhelming the system with constant rebuilds. This time frame enables enhanced learning by analyzing the effectiveness of the last adjustments and steadily refining the model without unnecessary interruptions.

Longer intervals like 30 or 60 minutes could lead to outdated responses if there are rapid changes in the data being processed, while shorter intervals such as 10 minutes may not provide enough time for the model to gain valuable insights from the information available. Hence, rebuilding every 20 minutes demonstrates an efficient approach to maintaining the model's relevance and performance during active coding in the queue.