During the training run, we made changes that had a positive impact, sometimes better than anticipated, which was an exciting moment for the team.

The ML code design didn't stop at launch; the team continued to work on issues and improvements actively, fostering a powerful spirit of teamwork.

The effort to parallelize work aggressively during the run was crucial to avoid performance cliffs that could make the model untrainable.

Resolving issues during the training run led to a significant performance boost, which energized the team and improved morale.

The evaluation process is continuous; it's not enough to just build a dataset and run it once. Ongoing evaluations are necessary to maintain quality in production systems.

The goal is to integrate subjective measurements into the model release pipeline, ensuring continuous evaluation post-release.

The goal is to integrate subjective measurements into the model release pipeline, ensuring continuous evaluation post-release.

Despite challenges, we had extensive planning for the run, starting a year before training began, which included careful sequencing of changes to minimize risks.

Control evaluations must be rerun with each new model to ensure that monitoring systems remain effective against potential threats.