GitHub - google-research/tuning_playbook: A playbook for systematically maximizing the performance of deep learning models.
github.com
GitHub - google-research/tuning_playbook: A playbook for systematically maximizing the performance of deep learning models.
BA Builder added
AI experiment management
Towards Recommender System Optimization: Our Data Tool for Algorithmic Optimization on Spotify [Part 1]
Music Tomorrowmusic-tomorrow.com![Thumbnail of Towards Recommender System Optimization: Our Data Tool for Algorithmic Optimization on Spotify [Part 1]](https://uploads-ssl.webflow.com/6206e1343aa2f122195717f8/6310b1a1447a22ac785a50ae_alina-grubnyak-ZiQkhI7417A-unsplash%20(1).jpg)
sari and added
MLEs are happy to delegate experiment tracking and execution work to ML experiment execution frameworks, such as Weights & Biases 3 , but prefer to choose subsequent experiments themselves. To be able to make informed choices of subsequent experiments to run, MLEs must maintain awareness of what they have tried and what they haven’t (Lg2 calls ... See more
Shreya Shankar • "We Have No Idea How Models will Behave in Production until Production": How Engineers Operationalize Machine Learning.
Nicolay Gerold added
learnings from one experiment into the next, like a guided search to find the best idea (Lg2, Sm4,
Lg5). Lg5 described their ideological shift from random search to guided search:
Previously, I tried to do a lot of parallelization. If I focus on one idea, a week at a time,
then it boosts my productivity a lot more.
By following a guided search, engineers are, essentially, significantly pruning a large subset of
experiment ideas without executing them. While it may seem like there are unlimited computational
resources, the search space is much larger, and developer time and energy is limited. At the end of
the day, experiments are human-validated and deployed. Mature ML engineers know their personal
tradeoff between parallelizing disjoint experiment ideas and pipelining ideas that build on top of
each other, ultimately yielding successful deployments
March 2023 AI Overview for Boards
docs.google.comsari and added
Good protocols, in short, manage to catalyze good enough outcomes with respect to a variety of contending criteria, via surprisingly limited and compact interventions.
Venkatesh Rao • The Unreasonable Sufficiency of Protocols
aron added
But mostly things have been discovered by trial and error, adding ideas and tricks that have progressively built a significant lore about how to work with neural nets.
Stephen Wolfram • What Is ChatGPT Doing ... And Why Does It Work?
Kaustubh Sule added
GitHub - karanpratapsingh/system-design: Learn how to design systems at scale and prepare for system design interviews
karanpratapsinghgithub.com