Harnessing AI to Optimize AI

Computational costs represent a significant barrier to model experimentation, especially for smaller organizations. In larger companies with ample computing resources, the risks associated with multiple model trials are reduced. Neural architecture search, a component of automated machine learning, aims to determine the most suitable AI models for specific prediction tasks, thereby minimizing computational demands. Instead of evaluating an exhaustive list of models, focusing on a small, informed subset can substantially reduce training costs. An example is Databricks, which recently reported spending only 10 million on model training, showcasing an efficient approach to fine-tuning large models.