deployment – Page 7 – Andrew Fairless, Ph.D.

What I Read: Building a Gigascale ML Feature Store

By Andrew Fairless on February 18, 2021January 18, 2021

https://doordash.engineering/2020/11/19/building-a-gigascale-ml-feature-store-with-redis/ Building a Gigascale ML Feature Store with Redis, Binary Serialization, String Hashing, and CompressionNovember 19, 2020 21 Minute Read Data, Machine Learning 36Arbaz Khan Zohaib Sibte Hassan “When aContinue readingWhat I Read: Building a Gigascale ML Feature Store

What I Read: Dynamic Data Testing

By Andrew Fairless on February 16, 2021January 18, 2021

https://medium.com/anomalo-hq/dynamic-data-testing-f831435dba90 Dynamic Data TestingJeremy StanleyNov 18 “Can’t we write down a clear set of rules that govern exactly how our data should behave, just like we do when testing software?Continue readingWhat I Read: Dynamic Data Testing

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: AI’s limitations

By Andrew Fairless on January 26, 2021December 31, 2020

https://www.economist.com/technology-quarterly/2020/06/11/an-understanding-of-ais-limitations-is-starting-to-sink-in Technology QuarterlyJun 11th 2020 editionAn understanding of AI’s limitations is starting to sink inAfter years of hype, many people feel AI has failed to deliver, says Tim Cross “SurveyingContinue readingWhat I Read: AI’s limitations

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: why Switch from Jupyter Notebook to Scripts

By Andrew Fairless on January 20, 2021December 28, 2020

https://towardsdatascience.com/5-reasons-why-you-should-switch-from-jupyter-notebook-to-scripts-cb3535ba9c95 5 Reasons why you should Switch from Jupyter Notebook to ScriptsUsing Scripts Helps me Realize the Drawbacks of Jupyter NotebookKhuyen TranAug 24 “If you have just switched from JupyterContinue readingWhat I Read: why Switch from Jupyter Notebook to Scripts

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: End-to-End Machine Learning Platforms

By Andrew Fairless on January 15, 2021December 24, 2020

https://databaseline.tech/a-tour-of-end-to-end-ml-platforms/ A Tour of End-to-End Machine Learning PlatformsIan Hellström21 February 2020 “What turns a collection of machine learning solutions into an end-to-end machine learning platform is an architecture that embracesContinue readingWhat I Read: End-to-End Machine Learning Platforms

What I Read: Snorkel Tutorial to Predict Multiple Sclerosis

By Andrew Fairless on January 13, 2021December 23, 2020

https://medium.com/swlh/a-snorkel-tutorial-using-clinical-notes-to-predict-multiple-sclerosis-severity-scores-e3863801630f A Snorkel Tutorial Using Clinical Notes to Predict Multiple Sclerosis Severity ScoresNLP4HJul 13 “In this tutorial, we will walk through the process of using Snorkel to generate labels forContinue readingWhat I Read: Snorkel Tutorial to Predict Multiple Sclerosis

What I Read: Intro to Data Engineering for Data Scientists

By Andrew Fairless on January 12, 2021December 23, 2020

https://towardsdatascience.com/intro-to-data-engineering-for-data-scientists-fa6c864a3ecc?gi=dd86c8bdeaa0 Intro to Data Engineering for Data ScientistsAn overview of data infrastructure which is frequently asked during interviewsWei WangJul 19 “…I have found that fresh data science graduates often don’tContinue readingWhat I Read: Intro to Data Engineering for Data Scientists

Tag: deployment