Kubernetes has rapidly evolved from theoretical trials to empirical deployments in an increasing number of US enterprises. However, the Chinese enterprises unveil different traits when it comes to req …

Everything changes at scale. Launching products at a scale of 1+ billion users requires a massive cross-team, cross-functional, coordinated effort, and business, engineering, and cultural challenges must be overcome. Kishore Jalleda and Gopal Mor explain how they have applied DevOps best practices at scale to successfully launch several high-profile products at Yahoo.

Software Heritage's mission is to collect, organize, preserve, and share the source code of all publicly available software. Roberto Di Cosmo surveys the motivations behind the launch of Software Heritage, which has already archived more than 3 billion unique source code files and 650 million unique commits, spanning more than 25 million FOSS projects from major software development hubs.

You need to think about financial data differently to solve the most pressing challenges for your organization. But how do you get to data-driven finance and risk using unstructured data? Michelle Bonat explains how unstructured data (words and text) can be used to solve critical challenges in finance and unlock opportunities.

Francisco Webber offers a critical overview of current approaches to artificial intelligence using "brute force" (aka big data machine learning) as well as a practical demonstration of semantic folding, an alternative approach based on computational principles found in the human neocortex. Semantic folding is not just a research prototypeit's a production-grade enterprise technology.

Quantopian integrates financial data from vendors around the globe. As the scope of its operations outgrew cron, the company turned to Apache Airflow, a distributed scheduler and task executor. James Meickle explains how in less than six months, Quantopian was able to rearchitect brittle crontabs into resilient, recoverable pipelines defined in code to which anyone could contribute.

Machines are not objective, and big data is not fair. Michael Williams uses sentiment analysis to show that supervised machine learning has the potential to amplify the voices of the most privileged people in society, violate the spirit and letter of civil rights law, and make your product suck.