technical debt – Andrew Fairless, Ph.D.

What I Read: Hidden Technical Debts

By Andrew Fairless on August 15, 2022July 18, 2022

https://www.kdnuggets.com/2022/07/hidden-technical-debts-every-ai-practitioner-aware.html Hidden Technical Debts Every AI Practitioner Should be Aware ofVidhi ChughJuly 7, 2022 “…technical debt is the long-term costs incurred by moving quickly in software engineering…. A large partContinue readingWhat I Read: Hidden Technical Debts

What I Read: Taxonomy of Tech Debt

By Andrew Fairless on May 3, 2022April 24, 2022

https://technology.riotgames.com/news/taxonomy-tech-debt A Taxonomy of Tech DebtApr 10, 2018 “This post will focus on types of tech debt I’ve seen during my time working at Riot, and a model for discussingContinue readingWhat I Read: Taxonomy of Tech Debt

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: Nitpicking ML Technical Debt

By Andrew Fairless on January 2, 2021December 22, 2020

https://matthewmcateer.me/blog/machine-learning-technical-debt/ Nitpicking Machine Learning Technical DebtRevisiting a resurging NeurIPS 2015 paper (and 25 best practices more relevant than that for 2020)May 10, 2020 “Now the Tech Debt Paper is makingContinue readingWhat I Read: Nitpicking ML Technical Debt

What I Read: DevOps for ML Data

By Andrew Fairless on December 23, 2020December 10, 2020

https://tecton.ai/blog/devops-ml-data/ Why We Need DevOps for ML DataPosted by Kevin Stumpf on April 28, 2020 “Unfortunately, most tools under the MLOps banner tend to focus only on workflows around theContinue readingWhat I Read: DevOps for ML Data

What I Read: Monitoring Machine Learning Models in Production

By Andrew Fairless on December 20, 2020December 23, 2020

https://christophergs.com/machine%20learning/2020/03/14/how-to-monitor-machine-learning-models/ Monitoring Machine Learning Models in ProductionA Comprehensive Guide14 March 2020 “Once you have deployed your machine learning model to production it rapidly becomes apparent that the work is notContinue readingWhat I Read: Monitoring Machine Learning Models in Production

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