testing – Andrew Fairless, Ph.D.

What I Read: Data Pipeline Smoke Tests

By Andrew Fairless on January 4, 2023November 5, 2022

https://dagster.io/blog/smoke-test-data-pipeline The Unreasonable Effectiveness of Data Pipeline Smoke TestsSandy RyzaOctober 19, 2022 “Data practitioners waste time writing unit tests to catch bugs they could have caught with smoke tests.”

What I Read: Testing Firefox, machine learning

By Andrew Fairless on September 29, 2022August 22, 2022

https://hacks.mozilla.org/2020/07/testing-firefox-more-efficiently-with-machine-learning/ Testing Firefox more efficiently with machine learningBy Andrew Halberstadt, Marco CastelluccioPosted on July 9, 2020 “Even with all of these optimizations our CI still runs around 10 compute yearsContinue readingWhat I Read: Testing Firefox, machine learning

What I Read: Data Observability vs. Data Testing

By Andrew Fairless on April 25, 2022March 12, 2022

https://towardsdatascience.com/data-observability-vs-data-testing-everything-you-need-to-know-6f3d7193b388?gi=6618bd7121fd Data Observability vs. Data Testing: Everything You Need to KnowYou already test your data. Do you need data observability, too?Lior GavishFeb 12 “In any data system, there are twoContinue readingWhat I Read: Data Observability vs. Data Testing

What I Read: ML model drift in production

By Andrew Fairless on March 21, 2022January 20, 2022

https://towardsdatascience.com/my-data-drifted-whats-next-how-to-handle-ml-model-drift-in-production-78719ef007b1 “My data drifted. What’s next?” How to handle ML model drift in production.An introductory overview of the possible steps.Elena SamuylovaDec 1, 2021 ““I have a model in production, andContinue readingWhat I Read: ML model drift in production

What I Watch: Engineering For Data

By Andrew Fairless on March 16, 2022January 20, 2022

https://youtu.be/jeMQld-6BtQ What Is Engineering For Data?Jan 5, 2022Continuous Delivery “What are the big mistakes that data scientists and big organisations often make when dealing with big data? What does engineeringContinue readingWhat I Watch: Engineering For Data

What I Read: Unit testing best practices

By Andrew Fairless on April 15, 2021April 2, 2021

https://docs.microsoft.com/en-us/dotnet/core/testing/unit-testing-best-practices Unit testing best practices with .NET Core and .NET StandardBy John Reese with special thanks to Roy Osherove07/28/2018 “There are numerous benefits to writing unit tests; they help withContinue readingWhat I Read: Unit testing best practices

What I Read: Building Robust Machine Learning Systems

By Andrew Fairless on February 19, 2021January 20, 2021

https://medium.com/swlh/deepminds-three-pillars-for-building-robust-machine-learning-systems-a9679e56250a DeepMind’s Three Pillars for Building Robust Machine Learning SystemsSpecification Testing, Robust Training and Formal Verification are three elements that the AI powerhouse believe hold the essence of robust machineContinue readingWhat I Read: Building Robust Machine Learning Systems

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: 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

Tag: testing