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

What I Read: MLOps Changing How Machine Learning Models Developed

By Andrew Fairless on February 26, 2021February 18, 2021

https://www.kdnuggets.com/2020/12/mlops-changing-machine-learning-developed.html MLOps Is Changing How Machine Learning Models Are DevelopedBy Henrik Skogstrom, Head of Growth at Valohai “Let’s look at three ways that MLOps changes how machine learning is developed:Continue readingWhat I Read: MLOps Changing How Machine Learning Models Developed

What I Watch: Functional Data Engineering

By Andrew Fairless on February 19, 2021January 18, 2021

https://youtu.be/4Spo2QRTz1k Functional Data Engineering – A Set of Best Practices | LyftMay 24, 2018 “…we’ll discuss functional programming paradigm and explore how applying it to Data Engineering can bring aContinue readingWhat I Watch: Functional Data Engineering

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: A winner’s curse adjustment

By Andrew Fairless on January 10, 2021December 23, 2020

http://www.erikdrysdale.com/winners_curse/ Erik DrysdalebioeconometRicianAbout Recent Posts ShinyApps CVA winner’s curse adjustment for a single test statistic “One consequence of using frequentist statistical tools to conduct scientific inference is that effect sizesContinue readingWhat I Read: A winner’s curse adjustment

What I Read: ML For Python Developers

By Andrew Fairless on January 8, 2021December 23, 2020

https://medium.com/modern-nlp/10-great-ml-practices-for-python-developers-b089eefc18fc 10 Useful ML Practices For Python DevelopersFast bug-free codingPratik BhavsarFeb 26 “Since my start, I have made tremendous mistakes and thought of sharing what I see to be theContinue readingWhat I Read: ML For Python Developers

What I Read: making machine learning actually useful

By Andrew Fairless on January 5, 2021December 22, 2020

https://www.shreya-shankar.com/making-ml-work/ Shreya ShankarReflecting on a year of making machine learning actually usefulJune 29, 2020 in #machine learning · 11 min read “…ML research as a field puts too much emphasisContinue readingWhat I Read: making machine learning actually useful

What I Read: Nitpicking ML Technical Debt

By Andrew Fairless on January 2, 2021December 22, 2020

https://matthewmcateer.me/blog/machine-learning-technical-debt/ Nitpicking Machine Learning Technical DebtRevisiting a resurging NeurIPS 2015 paper (and 25 best practices more relevant than that for 2020)May 10, 2020 “Now the Tech Debt Paper is makingContinue readingWhat I Read: Nitpicking ML Technical Debt

What I Read: Monitoring Machine Learning Models in Production

By Andrew Fairless on December 20, 2020December 23, 2020

https://christophergs.com/machine%20learning/2020/03/14/how-to-monitor-machine-learning-models/ Monitoring Machine Learning Models in ProductionA Comprehensive Guide14 March 2020 “Once you have deployed your machine learning model to production it rapidly becomes apparent that the work is notContinue readingWhat I Read: Monitoring Machine Learning Models in Production

What I Read: Reproducible Machine Learning

By Andrew Fairless on December 12, 2020December 1, 2020

https://thegradient.pub/independently-reproducible-machine-learning/ Independently Reproducible Machine Learning06.Feb.2020Edward Raff “How reproducible is the latest ML research, and can we begin to quantify what impacts its reproducibility? This question served as motivation for myContinue readingWhat I Read: Reproducible Machine Learning

What I Read: Reproducibility of Machine Learning in Health Care

By Andrew Fairless on December 1, 2020November 28, 2020

https://jamanetwork.com/journals/jama/fullarticle/2758612 Challenges to the Reproducibility of Machine Learning Models in Health CareAndrew L. Beam, PhD1,2; Arjun K. Manrai, PhD2,3; Marzyeh Ghassemi, PhD4,5 “In a lesson that is continuously learned, machineContinue readingWhat I Read: Reproducibility of Machine Learning in Health Care

Tag: reproducibility