https://medium.com/at-the-front-line/ml-observability-hype-or-here-to-stay-acef064ff843 ML Observability — Hype or Here to Stay?RuthOct 17 “Machine Learning Operations (MLOps) infrastructure is evolving at a phenomenal pace, and ML Observability is becoming a critical component of
What I Read: Realtime ML Pipelines
https://medium.com/@nparsons08/challenges-of-building-realtime-ml-pipelines-4782181425c7 Challenges of Building Realtime ML PipelinesNick ParsonsNov 18 “…as companies start introducing realtime into their ML pipelines, they are finding themselves having to weigh the trade-offs between performance, cost,
What I Read: Matrix Multiplication
https://www.quantamagazine.org/ai-reveals-new-possibilities-in-matrix-multiplication-20221123/ AI Reveals New Possibilities in Matrix MultiplicationBen BrubakerNovember 23, 2022 “Inspired by the results of a game-playing neural network, mathematicians have been making unexpected advances on an age-old math
What I Learn: video quality, neural networks
https://netflixtechblog.com/for-your-eyes-only-improving-netflix-video-quality-with-neural-networks-5b8d032da09c For your eyes only: improving Netflix video quality with neural networksby Christos G. Bampis, Li-Heng Chen and Zhi LiNetflix Technology BlogNov 14 “Recently, we added another powerful tool to
What I Learn: Preferences in Recommender Systems
https://medium.com/understanding-recommenders/what-does-it-mean-to-give-someone-what-they-want-the-nature-of-preferences-in-recommender-systems-82b5a1559157 What Does it Mean to Give Someone What They Want? The Nature of Preferences in Recommender SystemsLuke Thorburn, Jonathan Stray, Priyanjana BenganiMar 11 “We’ll propose… concrete ways to build
What I Read: Transformers Training
https://www.borealisai.com/research-blogs/tutorial-17-transformers-iii-training/ Tutorial #17: Transformers III Training08/06/2021P. Xu, S. Prince “…we discuss challenges with transformer training dynamics and introduce some of the tricks that practitioners use to get transformers to converge.”