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Data

Our take on the ideas, information, and tools that make data work.

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How will the GDPR impact machine learning?

By Andrew Burt | May 16, 2018

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Companies in China are moving quickly to embrace AI technologies

By Ben Lorica | May 10, 2018
Companies in China are moving quickly to embrace AI technologies
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Teaching and implementing data science and AI in the enterprise

By Ben Lorica | April 26, 2018
Teaching and implementing data science and AI in the enterprise
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The importance of transparency and user control in machine learning

By Ben Lorica | April 12, 2018
The importance of transparency and user control in machine learning
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Data engineers vs. data scientists

By Jesse Anderson | April 11, 2018

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What machine learning engineers need to know

By Ben Lorica | March 29, 2018
What machine learning engineers need to know
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How to train and deploy deep learning at scale

By Ben Lorica | March 15, 2018
How to train and deploy deep learning at scale
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Sprouted clams and stanky bean: When machine learning makes mistakes

By Janelle Shane | March 8, 2018

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Differentiating via data science

By Eric Colson | March 8, 2018

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Inclusivity for the greater good

By Ajey Gore | March 8, 2018

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Lessons in Google search data

By Seth Stephens-Davidowitz | March 8, 2018

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Highlights from the Strata Data Conference in San Jose 2018

By Mac Slocum | March 7, 2018

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Merging human and machine learning for everyday solutions

By Li Fan | March 7, 2018

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Privacy in the age of machine learning

By Ben Lorica | March 7, 2018

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Crisis Text Line data usage and insights

By Nancy Lublin, Bob Filbin | March 7, 2018

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Defining responsible data practices: A community-driven approach

By Natalie Evans Harris | March 7, 2018