Part 5/11:

• Data stratification: The dataset is partitioned into representative subsets, enhancing sampling efficiency.

• Model ranking: The system queries multiple leaderboards (e.g., MLPerf, industry-specific benchmarks) to identify the top 10 models relevant to the use case, then employs 4-bit quantized versions of these models for fine-tuning—substantially reducing computational costs.

• Model selection algorithm: A novel, custom algorithm iteratively searches for the most optimal model—balancing size, accuracy, and resource use—by applying a modified binary search mechanism over the list of potential models.

This process drastically reduces the number of experiments needed—from exponential trials to logarithmic complexity—thus saving considerable time and money.