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

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: Improving a Machine Learning System (Part 1 – Broken Abstractions)

By Andrew Fairless on December 22, 2021November 16, 2021

https://danshiebler.com/2021-11-06-ml-systems-1/ Improving a Machine Learning System (Part 1 – Broken Abstractions)Dan ShieblerPosted on November 6, 2021 “Making measurable improvements to a mature machine learning system is extremely difficult. In thisContinue readingWhat I Read: Improving a Machine Learning System (Part 1 – Broken Abstractions)

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 Learn: Scaling TensorFlow

By Andrew Fairless on October 12, 2021September 24, 2021

https://arxiv.org/abs/2109.09541 Scaling TensorFlow to 300 million predictions per secondJan Hartman, Davorin Kopič “In this work, we describe the process of scaling machine learning models implemented in the TensorFlow machine learningContinue readingWhat I Learn: Scaling TensorFlow

What I Read: introduction to machine learning compilers and optimizers

By Andrew Fairless on September 30, 2021September 21, 2021

https://huyenchip.com//2021/09/07/a-friendly-introduction-to-machine-learning-compilers-and-optimizers.html A friendly introduction to machine learning compilers and optimizersChip HuyenSep 7, 2021 “…especially when running an ML model on the edge, the model’s success still depends on the hardwareContinue readingWhat I Read: introduction to machine learning compilers and optimizers

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 dysfunctions of Data Engineering

By Andrew Fairless on September 9, 2021August 20, 2021

https://medium.com/@mrtrustworthy/from-data-driven-to-driving-data-the-dysfunctions-of-data-engineering-34c34496ed8e From Data Driven to Driving Data— The dysfunctions of Data EngineeringMrTrustworthyJul 27 “Being Data Driven isn’t enough…. We need Data Engineering… as a methodical approach at re-designing how aContinue readingWhat I Read: The dysfunctions of Data Engineering

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