Posted by Jason Jabbour, Kai Kleinbard and Vijay Janapa Reddi (Harvard University)Everyone wants to do the modeling work, but no one wants to do the engineering.| The TensorFlow Blog
Since 2010, the length of training runs has increased by 1.2x per year among notable models, excluding those that are fine-tuned from base models. A continuation of this trend would ease hardware constraints, by increasing training compute without requiring more chips or power. However, longer training times face a tradeoff. For very long runs, waiting for future improvements to algorithms and hardware might outweigh the benefits of extended training.| Epoch AI
Learn how Tecton solves hidden data engineering challenges in ML, from pipeline building to ensuring data consistency, to accelerate AI development.| Tecton
Discover how Tecton accelerates ML development with automated feature engineering, rapid experimentation, and streamlined deployment for smarter models.| Tecton