MLOps – Page 5 – Andrew Fairless, Ph.D.

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: Accelerating ML within CNN

By Andrew Fairless on June 3, 2021May 14, 2021

https://medium.com/cnn-digital/accelerating-ml-within-cnn-983f6b7bd2eb Accelerating ML within CNNKelly DavisMay 11 “Unfortunately, it was not designed for lightweight experimentation, and because of that, the research iteration process was frustratingly slow… we explored a numberContinue readingWhat I Read: Accelerating ML within CNN

What I Read: Feature stores

By Andrew Fairless on May 31, 2021May 14, 2021

https://www.kdnuggets.com/2021/05/feature-stores-how-avoid-feeling-every-day-is-groundhog-day.html Feature stores – how to avoid feeling that every day is Groundhog DayBy Monte Zweben, CEO, Splice Machine. “Feature stores stop the duplication of each task in the MLContinue readingWhat I Read: Feature stores

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 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: Machine learning is going real-time

By Andrew Fairless on March 3, 2021March 3, 2021

https://huyenchip.com/2020/12/27/real-time-machine-learning.html Machine learning is going real-timeDec 27, 2020Chip Huyen “Machine learning is going real-time, whether you’re ready or not. While the majority of companies are still debating whether there’s valueContinue readingWhat I Read: Machine learning is going real-time

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 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: Data Scientists Should Be More End-to-End

By Andrew Fairless on January 17, 2021December 26, 2020

https://eugeneyan.com/writing/end-to-end-data-science/ Unpopular Opinion – Data Scientists Should Be More End-to-End “Eric Colson of Stitch Fix was initially “lured to a function-based division of labour by the attraction of process efficiencies”…Continue readingWhat I Read: Data Scientists Should Be More End-to-End

Tag: MLOps