container – Andrew Fairless, Ph.D.

What I Read: What we don’t talk about

By Andrew Fairless on August 28, 2023July 30, 2023

https://veekaybee.github.io/2023/07/18/what-we-dont-talk-about-when-we-talk-about-building-ai-apps/ What we don’t talk about when we talk about building AI appsVicki Boykis7/17/23 8:00 PM “From the technical perspective, here’s what we don’t discuss when we talk about deepContinue readingWhat I Read: What we don’t talk about

What I Read: Kubernetes, Batch

By Andrew Fairless on July 25, 2023June 15, 2023

https://betterprogramming.pub/kubernetes-was-never-designed-for-batch-jobs-f59be376a338 Kubernetes Was Never Designed for Batch JobsAnd what it would look like if it had beenMeadowrunSep 1, 2022 “…Kubernetes is philosophically biased towards microservices over batch jobs. This resultsContinue readingWhat I Read: Kubernetes, Batch

What I Read: Optimizing Machine Learning Training Pipelines

By Andrew Fairless on March 15, 2023February 4, 2023

https://medium.com/ntropy-network/bag-of-tricks-for-optimizing-machine-learning-training-pipelines-4f8d5cd3d432 Bag of tricks for optimizing machine learning training pipelinesArseny KravchenkoJan 5 “…we are constantly looking for ways to improve the efficiency of our machine learning pipelines, while keeping theContinue readingWhat I Read: Optimizing Machine Learning Training Pipelines

What I Read: Dev and Data Science Independence

By Andrew Fairless on December 13, 2022November 5, 2022

https://medium.com/riskified-technology/how-we-enabled-dev-and-data-science-independence-with-clear-api-boundaries-3a6a28597746 How We Enabled Dev and Data Science Independence With Clear API Boundaries Using Airflow and DatabricksUri BrodskyOct 18 “In this post, I will show you how we enabled ourContinue readingWhat I Read: Dev and Data Science Independence

What I Read: The Uselessness of Useful Knowledge

By Andrew Fairless on November 2, 2021October 24, 2021

https://www.quantamagazine.org/science-has-entered-a-new-era-of-alchemy-good-20211020/ The Uselessness of Useful KnowledgeRobbert DijkgraafOctober 20, 2021Today’s powerful but little-understood artificial intelligence breakthroughs echo past examples of unexpected scientific progress.“…the current state of AI research is nothing newContinue readingWhat I Read: The Uselessness of Useful Knowledge

What I Read: What I’ve learned about MLOps

By Andrew Fairless on June 23, 2021June 7, 2021

https://ramseyelbasheer.io/2021/05/17/what-ive-learned-about-mlops-from-speaking-with-100-ml-practitioners/ What I’ve learned about MLOps from speaking with 100+ ML practitionersby Ramsey ElbasheerMay 17, 2021 “Without further ado, here is part of the key takeaways around the everyday MLOpsContinue readingWhat I Read: What I’ve learned about MLOps

What I Read: Productizing Machine Learning Models

By Andrew Fairless on June 14, 2021May 30, 2021

https://blog.sasken.com/productizing-machine-learning-models-an-introduction Productizing Machine Learning Models – An IntroductionFeb 11, 2020 4:36:36 AM “This post will explore some of the challenges in placing ML models in a production environment. It willContinue readingWhat I Read: Productizing Machine Learning Models

What I Read: Conda Tips

By Andrew Fairless on May 26, 2021May 1, 2021

https://towardsdatascience.com/conda-essential-concepts-and-tricks-e478ed53b5b Conda: Essential Concepts and TipsFor beginners and experienced usersGiacomo VianelloMar 17 “I hope this gives you a hint of why conda is such an amazing tool. Let’s jump intoContinue readingWhat I Read: Conda Tips

What I Read: Where Programming, Ops, AI, and the Cloud are Headed

By Andrew Fairless on March 18, 2021March 7, 2021

https://www.oreilly.com/radar/where-programming-ops-ai-and-the-cloud-are-headed-in-2021/ Where Programming, Ops, AI, and the Cloud are Headed in 2021Following O’Reilly online learning trends to see what’s coming next.By Mike LoukidesJanuary 25, 2021 “Operations staff too frequently assumeContinue readingWhat I Read: Where Programming, Ops, AI, and the Cloud are Headed

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

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