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Much has been written about struggles of deploying machine learning projects to production. As with many burgeoning fields and disciplines, we don’t yet have a shared canonical infrastructure stack or best practices for developing and deploying data-intensive applications. This is both frustrating for companies that would prefer making ML...
The Sobering Truth About the Impact of Your Business Ideas – O’Reilly
The introduction of data science into the business world has contributed far more than recommendation algorithms; it has also taught us a lot about...
Where Programming, Ops, AI, and the Cloud are Headed in 2021 – O’Reilly
In this report, we look at the data generated by the O’Reilly online learning platform to discern trends in the technology industry—trends technology leaders...
Four short links: 14 Dec 2020
End-to-end Entity Resolution for Big Data — Introduction to the entity resolution pipeline and the algorithms at the different stages. Includes a summary...
Radar trends to watch: January 2021 – O’Reilly
The last month of the old year showed a lot of activity on the border of AI and biology. The advances in protein folding...
The Quality of Auto-Generated Code – O’Reilly
Kevlin Henney and I were riffing on some ideas about GitHub Copilot, the tool for automatically generating code base on GPT-3’s language model, trained...
Seven Legal Questions for Data Scientists – O’Reilly
“he threats to consumers arising from data abuse, including those posed by algorithmic harms, are mounting and urgent.”
FTC Commissioner Rebecca K. Slaughter
Variants of artificial...
Why Data Makes It Different – O’Reilly
Much has been written about struggles of deploying machine learning projects to production. As with many burgeoning fields and disciplines, we don’t yet have...
