All Articles – O’Reilly

So, you want to be successful in the open future?

By Louise Beaumont

Louise Beaumont explores the five characteristics of companies that choose to succeed.

Machine learning: Research & industry

By Mikio Braun

Having worked in both research and industry, Mikio Braun shares insights into what's the same, what's different, and how deep learning might change the game.

When to KISS

By Zubin Siganporia

Zubin Siganporia explains how the KISS principle (“Keep It Simple, Stupid”) applies to solving problems and convincing end-users to adopt data-driven solutions to their challenges.

Out of the lab and into real life

By Christine Foster

Christine Foster discusses how today’s academic papers turn into tomorrow’s data science.

The good, the bad, and the internet?

By Martha Lane Fox

Martha Lane Fox considers the unintended consequences of technology.

7 questions to ask before you launch an enterprise blockchain project

By Mike Loukides

Successful projects will think seriously about what blockchains mean, and how to use them effectively.

Four short links: 24 May 2018

By Nat Torkington

Biosynthesising Nanomaterials, OS X Age, Debugging Machine Learning, and Deepfake Detection

Highlights from the Strata Data Conference in London 2018

By Mac Slocum

Watch highlights covering machine learning, GDPR, data protection, and more. From the Strata Data Conference in London 2018.

Charting a data journey to the cloud

By Mick Hollison, Sven Löffler and Robert Neumann

Mick Hollison, Sven Löffler, and Robert Neumann explain how Deutsche Telekom is harnessing machine learning and analytics in the cloud to build Europe’s largest IoT data marketplace.

Journey to GDPR compliance

By Alison Howard

May 25 is an important day for data protection in the EU and elsewhere. Alison Howard explains how Microsoft has prepared for May 25 and beyond.

Building a stronger data ecosystem

By Ben Lorica

Ben Lorica looks at the problems we’re facing as we collect and store data, particularly when our machine learning models require huge amounts of labeled data.