Navigating Challenges in AI Model Optimization

Exploring the hurdles users encounter in fine-tuning AI models within constraints of time and expertise, weighing the benefits of large language models versus synthetic data in specialized scenarios, and discussing techniques like data curation and model merging. Addressing the struggle in managing time efficiently while educating teams on AI models, considering a dual approach of technical content and manual onboarding, and aiming for a self-serving process amidst the industry's rapid evolution. Analyzing the impact of synthetic data on model accuracy, detailing the process of implementing synthetic data from use case description to model training, and comparing user engagement challenges in using synthetic data versus traditional data cleaning in AI models.