Modifying Foundation Models
Change management plays a crucial role in the successful implementation of AI applications within enterprises. Fine tuning is highlighted as a key method for modifying foundation models, allowing businesses to adapt these models to their specific needs without the high costs associated with building from scratch. Continuous pre-training is also discussed as a way to keep models updated with the latest information relevant to their use cases.In this clip
From this podcast
Super Data Science: ML & AI Podcast with Jon Krohn
853: Generative AI for Business — with Kirill Eremenko and Hadelin de Ponteves
Related Questions
How does AI fine-tuning work?
Can you explain more about how AI models are trained?
I have a question about the episode Navigating Machine Learning Careers: Insights from Meta to Consulting // Ilya Reznik // #286 and the clip Fine Tuning Insights. Can you explain the differences between fine-tuning and training models: LLMs vs custom model applications and when to use each?