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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

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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

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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

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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

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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

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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

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“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

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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...

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