Part 4/11:

• Trial-and-error nature: Fine-tuning hyperparameters across multiple models leads to extensive iterations, inflating costs and extending timelines.

The team emphasizes that traditional approaches—manual trial of multiple models, hyperparameter tuning, and evaluation—are no longer feasible or scalable amidst the current AI explosion.

A Data-Driven, Unified Framework

To tackle these challenges, the team developed a comprehensive pipeline designed to automate and optimize model selection and fine-tuning. The approach involves:

• User input: The end-user supplies a specific use case, a dataset, a minimum desired accuracy threshold (e.g., 80-100%), and an evaluation script.