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

What I Read: The one data platform to rule them all

By Andrew Fairless on August 26, 2021August 17, 2021

https://leighmariebraswell.substack.com/p/the-one-data-platform-to-rule-them The one data platform to rule them all… but according to whom?An overview of recent data startups based on popular end usersLeigh Marie BraswellJul 20 “…seeing so many “dataContinue readingWhat I Read: The one data platform to rule them all

What I Read: What is a Data Mesh?

By Andrew Fairless on July 6, 2021June 22, 2021

https://cnr.sh/essays/what-the-heck-data-mesh What the Heck is a Data Mesh?!Chris Riccomini on June 8, 2021 “Yet, organizations don’t think of data as a product…. It would be madness to propose centralizing allContinue readingWhat I Read: What is a Data Mesh?

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: Models of Data Science teams

By Andrew Fairless on May 11, 2021April 17, 2021

https://www.marcosantoni.com/chess_vs_checkers_teams.html Marco SantoniModels of Data Science teams: Chess vs Checkers “One of the key decisions to take when building a data science team is the mix of roles…. I’ll compareContinue readingWhat I Read: Models of Data Science teams

What I Watch: Regaining Control in Deep Systems

By Andrew Fairless on April 5, 2021March 21, 2021

https://youtu.be/xCRFlhkAF1s Architectures That Scale Deep – Regaining Control in Deep SystemsMar 15, 2021InfoQ “Ben Sigelman talks about “Deep Systems”, their common properties and re-introduces the fundamentals of control theory fromContinue readingWhat I Watch: Regaining Control in Deep Systems

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: MLOps for effective AI strategy

By Andrew Fairless on March 14, 2021March 7, 2021

https://www.sigmoid.com/blogs/mlops-for-effective-ai-strategy/ SigmoidHow to use MLOps for an effective AI strategy “One of the foremost challenges in implementing MLOps is the difficulty in superimposing DevOps practices on ML pipelines. This isContinue readingWhat I Read: MLOps for effective AI strategy

What I Read: What is Data Observability?

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

https://towardsdatascience.com/what-is-data-observability-40b337971e3e What is Data Observability?Hint: it’s not just data for DevOps.Barr MosesJun 25 “Instead of putting together a holistic approach to address data downtime, teams often tackle data quality andContinue readingWhat I Read: What is Data Observability?

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

Tag: DevOps