MLOps – Page 4 – Andrew Fairless, Ph.D.

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: The Uselessness of Useful Knowledge

By Andrew Fairless on November 2, 2021October 24, 2021

https://www.quantamagazine.org/science-has-entered-a-new-era-of-alchemy-good-20211020/ The Uselessness of Useful KnowledgeRobbert DijkgraafOctober 20, 2021Today’s powerful but little-understood artificial intelligence breakthroughs echo past examples of unexpected scientific progress.“…the current state of AI research is nothing newContinue readingWhat I Read: The Uselessness of Useful Knowledge

What I Read: Systems for Machine Learning

By Andrew Fairless on September 14, 2021August 20, 2021

https://thegradient.pub/systems-for-machine-learning/ Systems for Machine LearningKabir Nagrecha14.Aug.2021 “Machine learning’s increasing importance to real-world applications brought awareness of a new field focused on ML in practice – machine learning systems (or, asContinue readingWhat I Read: Systems for Machine Learning

What I Read: The one data platform to rule them all

By Andrew Fairless on August 26, 2021August 17, 2021

https://leighmariebraswell.substack.com/p/the-one-data-platform-to-rule-them The one data platform to rule them all… but according to whom?An overview of recent data startups based on popular end usersLeigh Marie BraswellJul 20 “…seeing so many “dataContinue readingWhat I Read: The one data platform to rule them all

What I Read: What I’ve learned about MLOps

By Andrew Fairless on June 23, 2021June 7, 2021

https://ramseyelbasheer.io/2021/05/17/what-ive-learned-about-mlops-from-speaking-with-100-ml-practitioners/ What I’ve learned about MLOps from speaking with 100+ ML practitionersby Ramsey ElbasheerMay 17, 2021 “Without further ado, here is part of the key takeaways around the everyday MLOpsContinue readingWhat I Read: What I’ve learned about MLOps

What I Read: Productizing Machine Learning Models

By Andrew Fairless on June 14, 2021May 30, 2021

https://blog.sasken.com/productizing-machine-learning-models-an-introduction Productizing Machine Learning Models – An IntroductionFeb 11, 2020 4:36:36 AM “This post will explore some of the challenges in placing ML models in a production environment. It willContinue readingWhat I Read: Productizing Machine Learning Models

What I Read: Accelerating ML within CNN

By Andrew Fairless on June 3, 2021May 14, 2021

https://medium.com/cnn-digital/accelerating-ml-within-cnn-983f6b7bd2eb Accelerating ML within CNNKelly DavisMay 11 “Unfortunately, it was not designed for lightweight experimentation, and because of that, the research iteration process was frustratingly slow… we explored a numberContinue readingWhat I Read: Accelerating ML within CNN

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

What I Read: Models of Data Science teams

By Andrew Fairless on May 11, 2021April 17, 2021

https://www.marcosantoni.com/chess_vs_checkers_teams.html Marco SantoniModels of Data Science teams: Chess vs Checkers “One of the key decisions to take when building a data science team is the mix of roles…. I’ll compareContinue readingWhat I Read: Models of Data Science teams

Tag: MLOps