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

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: How Build Production Workflow SQL

By Andrew Fairless on February 22, 2021January 27, 2021

https://shopify.engineering/build-production-grade-workflow-sql-modelling How to Build a Production Grade Workflow with SQL Modellingby Michelle Ark Data Science & EngineeringNov 19, 2020 “We’ll show you how we moved to a SQL modelling workflowContinue readingWhat I Read: How Build Production Workflow SQL

What I Read: Software Tips for Data Science

By Andrew Fairless on February 2, 2021January 8, 2021

https://medium.com/swlh/software-engineering-tips-and-best-practices-for-data-science-5d85dbcf87fd Software Engineering Tips and Best Practices for Data ScienceWith great code comes great machine learningAhmed BesbesAug 28 “However, jupyter notebooks hide some serious pitfalls that may turn your codingContinue readingWhat I Read: Software Tips for Data Science

What I Read: data quality, ML Ops

By Andrew Fairless on January 27, 2021January 1, 2021

https://greatexpectations.io/blog/ml-ops-data-quality/ Why data quality is key to successful ML OpsIn this first post in our 2-part ML Ops series, we are going to look at ML Ops and highlight howContinue readingWhat I Read: data quality, ML Ops

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: Data Scientists Should Be More End-to-End

By Andrew Fairless on January 17, 2021December 26, 2020

https://eugeneyan.com/writing/end-to-end-data-science/ Unpopular Opinion – Data Scientists Should Be More End-to-End “Eric Colson of Stitch Fix was initially “lured to a function-based division of labour by the attraction of process efficiencies”…Continue readingWhat I Read: Data Scientists Should Be More End-to-End

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