Part 7/11:

• Outcome: The algorithm converges onto the minimal model size that satisfies the threshold, providing both the name of the chosen model and optimal hyperparameters.

This novel approach drastically cuts down the hyperparameter tuning experiments needed, often from dozens or hundreds to just a few, leveraging a logarithmic search strategy (O(log n)), ensuring efficiency proportional to the model repository size.

Experimental Validation and Results

The platform was validated through experiments on email summarization, with promising results:

• Starting with 10 candidate models ranked by size (from small to large).

• The algorithm applied three main steps to identify the optimal model, achieving the target accuracy (e.g., 90%) with minimal hyperparameter tuning.