Featured Articles

Featured

AI chips for big data and machine learning: Hard choices in the cloud and on-premise

How can GPUs and FPGAs help with data-intensive tasks such as operations, analytics, and machine…

Aug 20, 2018

Zen and the art of data structures: From self-tuning to self-designing data systems

Designing data systems is something few people understand, and it’s very hard and costly. But…

Aug 6, 2018

The best programming language for data science and machine learning

Hint: There is no easy answer, and no consensus either. Arguing about which programming language…

Jul 19, 2018

GraphQL for databases: A layer for universal database access?

GraphQL is a query language mostly used to streamline access to REST APIs. Now, a…

Jun 19, 2018

AWS Neptune going GA: The good, the bad, and the ugly for graph database users and vendors

It’s official: AWS has a production-ready graph database. What features are included today, and what…

May 31, 2018

GDPR in real life: Transparency, innovation, and adoption across borders and organizations

Part two: Auditing data on premise and in the cloud, spurring innovation in machine learning…

May 24, 2018

Human in the loop: Machine learning and AI for the people

HITL is a mix and match approach that may help make ML both more efficient…

May 23, 2018

GDPR in real life: Fear, uncertainty, and doubt

Part one: Why are most organizations still not ready for GDPR? And what are the…

May 18, 2018

The road to automation, the joy of work, and the ‘Jen problem’

How do we get to the point where technology is the infrastructure firms run on,…

May 1, 2018

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