monitoring – Page 2 – Andrew Fairless, Ph.D.

What I Read: Bundling into the Database

By Andrew Fairless on July 5, 2022June 27, 2022

https://www.ethanrosenthal.com/2022/05/10/database-bundling/ Let’s Continue Bundling into the DatabaseEthan RosenthalMay 10, 2022 “Truthfully, I just want to argue that feature stores, metrics layers, and machine learning monitoring tools are all abstraction layersContinue readingWhat I Read: Bundling into the Database

What I Read: Modern Stack for ML Infrastructure

By Andrew Fairless on June 13, 2022May 23, 2022

https://youtu.be/gZnhSHvhuFQThe Modern Stack for ML Infrastructure | OuterboundsMay 2, 2022Data Council “Metaflow was originally developed at Netflix to provide a user-friendly platform for a wide range of ML use cases…Continue readingWhat I Read: Modern Stack for ML Infrastructure

What I Read: Data Distribution Shifts

By Andrew Fairless on April 6, 2022March 12, 2022

https://huyenchip.com//2022/02/07/data-distribution-shifts-and-monitoring.html Data Distribution Shifts and MonitoringChip HuyenFebruary 7 2022 12:00 AM “…deploying a model isn’t the end of the process. A model’s performance degrades over time in production. Once aContinue readingWhat I Read: Data Distribution Shifts

What I Read: MLOps Documentation

By Andrew Fairless on February 2, 2022January 20, 2022

https://www.kdnuggets.com/2021/12/best-practices-mlops-docs.html Best Practices for MLOps DocumentationBy Nifesimi Ademoye “Why does technical documentation matter in MLOps?…. MLOps levels are defined by the extent of automation of processes in an ML pipeline,Continue readingWhat I Read: MLOps Documentation

What I Read: Maps of Model Space, Stan

By Andrew Fairless on January 5, 2022December 11, 2021

https://statmodeling.stat.columbia.edu/2021/11/19/drawing-maps-of-model-space-with-modular-stan/ Drawing Maps of Model Space with Modular StanPosted on November 19, 2021 9:50 AM by AndrewThis post is by Ryan Bernstein. “What if instead of describing one model atContinue readingWhat I Read: Maps of Model Space, Stan

What I Read: AntiPatterns, MLOps

By Andrew Fairless on January 4, 2022December 11, 2021

https://arxiv.org/abs/2107.00079 Using AntiPatterns to avoid MLOps MistakesNikhil Muralidhar, Sathappah Muthiah, Patrick Butler, Manish Jain, Yu Yu, Katy Burne, Weipeng Li, David Jones, Prakash Arunachalam, Hays ‘Skip’ McCormick, Naren Ramakrishnan “HereContinue readingWhat I Read: AntiPatterns, MLOps

What I Read: Lessons on ML Platforms

By Andrew Fairless on December 20, 2021November 16, 2021

https://towardsdatascience.com/lessons-on-ml-platforms-from-netflix-doordash-spotify-and-more-f455400115c7 Lessons on ML Platforms — from Netflix, DoorDash, Spotify, and moreErnest ChanMay 12 “Many companies who do ML well have invested in ML platforms to enable data scientists toContinue readingWhat I Read: Lessons on ML Platforms

What I Read: Building Data Platform

By Andrew Fairless on August 12, 2021July 22, 2021

https://www.montecarlodata.com/the-quick-and-dirty-guide-to-building-your-data-platform/ Updated July 9, 2021The Quick and Dirty Guide to Building Your Data PlatformBarr Moses, Lior Gavish“For most organizations, building a data platform is no longer a nice-to-have but aContinue readingWhat I Read: Building Data Platform

What I Read: Can Model Monitor Another Model?

By Andrew Fairless on June 28, 2021June 7, 2021

https://towardsdatascience.com/can-you-build-a-machine-learning-model-to-monitor-another-model-15ad561d26df Can You Build a Machine Learning Model to Monitor Another Model?Emeli DralMay 28, 2021 “Can you train a machine learning model to predict your model’s mistakes? Nothing stops youContinue readingWhat I Read: Can Model Monitor Another Model?

What I Read: Feature stores

By Andrew Fairless on May 31, 2021May 14, 2021

https://www.kdnuggets.com/2021/05/feature-stores-how-avoid-feeling-every-day-is-groundhog-day.html Feature stores – how to avoid feeling that every day is Groundhog DayBy Monte Zweben, CEO, Splice Machine. “Feature stores stop the duplication of each task in the MLContinue readingWhat I Read: Feature stores

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