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Many of today’s most popular AI systems are, at their core, classifiers. They classify inputs into different categories: this image is a picture of a dog, not a cat; this audio signal is an instance of the word “Boston”, not the word “Seattle”; this sentence is a request to play a video, not a song. But what happens if you need to add a new class to your classifier — if, say, someone releases a new type of automated household appliance that your smart-home system needs to be able to control?

Hi, I’m Guy ‘RiotSomeOtherGuy’ Kisel, a software engineer at Riot. You might remember me from Running an Automated Test Pipeline for the League Client Update. I work on the Riot Developer Experience team – our responsibilities include providing Jenkins servers and related infrastructure for engineers to use for building, testing, and shipping their software to the millions of players that play League of Legends. We’re sort of like a traditional build team, except that we provide self-service tools for engineers to manage their own builds instead of building their software for them.

At Uber, real-time analytics allow us to attain business insights and operational efficiency, enabling us to make data-driven decisions to improve experiences on the Uber platform. For example, our operations team relies on data to monitor the market health and spot potential issues on our platform; software powered by machine learning models leverages data to predict rider supply and driver demand; and data scientists use data to improve machine learning models for better forecasting.

Back in 2011, when the venture capitalist Marc Andreessen said that “software is eating the world,” it was still a fresh idea. Now it’s obvious that software permeates our lives. From complex electronics like medical devices and autonomous vehicles to simple objects like Internet-connected lightbulbs and thermometers, we’re surrounded by software. And that means we’re all more exposed to attacks on that software than everbefore. Every year, 111 billion lines are added to the mass of software code in existence, and every line presents a potential new target.

At Soluto, we have super-devs who have full ownership: from writing code to deploying it to monitoring. When we made the shift to Kubernetes, we wanted to keep our devs independent and put a lot of effort into allowing them to create services rapidly. It all worked like a charm – until they had to handle credentials. This challenge leads us to build Kamus – an open source, GitOps, zero trust, secrets solution for Kubernetes applications.

This is brief summary of parts of my master’s thesis and the conclusions to draw from it. This medium-story focuses on containerized application isolation. The thesis also covers segmentation of cluster networks in Kubernetes which is not discussed in this story. You can read my full thesis here; it’s available through open access:Container Orchestration in Security Demanding Environments at the Swedish Police Authority. Source: medium.com

In this post I will show you how to use Roles published to Ansible Galaxy as an Operator to manage an application in Kubernetes. Reusing a Role in this way provides an example of how to create an Operator that simply installs an application with the flexibility to expand and customize the behavior organically as requirements dictate. I will leverage both the Ansible Operator and the k8s module to demonstrate how you can use Ansible to create Kubernetes native applications.

Sapienz and SapFix are automated tools that Facebook now uses to find and fix problems across all of the company’s apps Facebook’s Getafix tool serves up templates for how to fix common coding errors to its SapFix system, which then recommends solutions to developers. A null pointer exception is like having the address to a house that was never built. It means a programmer has referred to an object that doesn’t actually exist because it was never described in the code.

SAN FRANCISCO, Calif., January 24, 2018 – The Cloud Native Computing Foundation® (CNCF®), which sustains open source technologies like Kubernetes® and Prometheus™, today announced that CoreDNS is the first project of 2019 to graduate, following last year’s graduations of Kubernetes, Prometheus, and Envoy. To move from the maturity level of incubation to graduation, projects must demonstrate thriving adoption, diversity, a formal governance process, and a strong commitment to community sustainability and inclusivity.

Machine learning systems often act on “features” extracted from input data. In a natural-language-understanding system, for instance, the features might include words’ parts of speech, as assessed by an automatic syntactic parser, or whether a sentence is in the active or passive voice. Some machine learning systems could be improved if, rather than learning from extracted features, they could learn directly from the structure of the data they’re processing.