monitoring – Page 3 – Andrew Fairless, Ph.D.

What I Read: Continuous Training Strategy

By Andrew Fairless on May 12, 2021April 17, 2021

https://www.superwise.ai/resources/framework-for-a-successful-continuous-training-strategy Framework for a successful Continuous Training StrategyOr Itzary & Liran NahumMarch 18, 2021 “…ML models need to be retrained regularly…. Continuous training seeks to automatically and continuously retrain theContinue readingWhat I Read: Continuous Training Strategy

What I Read: Where Programming, Ops, AI, and the Cloud are Headed

By Andrew Fairless on March 18, 2021March 7, 2021

https://www.oreilly.com/radar/where-programming-ops-ai-and-the-cloud-are-headed-in-2021/ Where Programming, Ops, AI, and the Cloud are Headed in 2021Following O’Reilly online learning trends to see what’s coming next.By Mike LoukidesJanuary 25, 2021 “Operations staff too frequently assumeContinue readingWhat I Read: Where Programming, Ops, AI, and the Cloud are Headed

What I Read: definitive guide to AI monitoring

By Andrew Fairless on March 7, 2021March 7, 2021

https://towardsdatascience.com/the-definitive-guide-to-ai-monitoring-2427812cc1b?gi=d1344496bc82 The definitive guide to AI monitoringLearning from our work creating production visibility for teams across deep learning and machine learning use casesYotam OrenNov 3, 2020 “AI teams across verticalsContinue readingWhat I Read: definitive guide to AI monitoring

What I Read: Deploying Machine Learning, a Survey of Case Studies

By Andrew Fairless on February 23, 2021February 3, 2021

https://arxiv.org/abs/2011.09926 Challenges in Deploying Machine Learning: a Survey of Case StudiesAndrei Paleyes, Raoul-Gabriel Urma, Neil D. Lawrence “This survey reviews published reports of deploying machine learning solutions in a varietyContinue readingWhat I Read: Deploying Machine Learning, a Survey of Case Studies

What I Read: Best Practices for Building Machine Learning at Scale

By Andrew Fairless on January 24, 2021December 30, 2020

https://medium.com/dataseries/linkedins-pro-ml-architecture-summarizes-best-practices-for-building-machine-learning-at-scale-77fcb6afc9ec LinkedIn’s Pro-ML Architecture Summarizes Best Practices for Building Machine Learning at ScaleThe reference architecture is powering mission critical machine learning workflows within LinkedIn.Jesus RodriguezSep 17 “Building machine learning solutionsContinue readingWhat I Read: Best Practices for Building Machine Learning at Scale

What I Read: Maintaining Machine Learning in Production

By Andrew Fairless on January 19, 2021December 27, 2020

https://eugeneyan.com/writing/practical-guide-to-maintaining-machine-learning/ A Practical Guide to Maintaining Machine Learning in Production “This follow-up will share some practices I’ve found useful to maintaining machine learning in production. They are: Monitor Training andContinue readingWhat I Read: Maintaining Machine Learning in Production

What I Read: Running Machine Learning at Scale

By Andrew Fairless on January 7, 2021December 23, 2020

https://medium.com/@jrodthoughts/some-things-uber-learned-from-running-machine-learning-at-scale-70dccdfb944d Some Things Uber Learned from Running Machine Learning at ScaleUber machine learning runtime Michelangelo has been in operation for a few years. What has the Uber team learned?Jesus RodriguezJulContinue readingWhat I Read: Running Machine Learning at Scale

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: To Adapt to Tech, We’re Heading Into the Shadows

By Andrew Fairless on December 30, 2020December 20, 2020

https://www.wired.com/story/opinion-to-adapt-to-tech-were-heading-into-the-shadows/ Matt BeaneIdeas06.08.2020 09:59 AMTo Adapt to Tech, We’re Heading Into the ShadowsMore and more innovation requires going into darker, more inappropriate, less ethical territory. Here’s how to respond. “…I’veContinue readingWhat I Read: To Adapt to Tech, We’re Heading Into the Shadows

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

Tag: monitoring