Tag: Automate

Continuous Integration Leave a comment

Why Credit Karma Crafted a Tool to Automate Its DevOps Cycle

Source:-https://www.informationweek.com Jeremy Unruh, head of developer efficiency and platform engineering for Credit Karma, says one of his team’s goals is to speed up the company’s engineering. Given the complexities of many platforms and the technologies they use such as Kubernetes, he says the typical product engineer must focus on shipping features. That can include experimenting and shifting traffic before customers see those features, Unruh says. “Part of my team’s motive is to abstract that away and provide a seamless experience

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Kubernetes Leave a comment

Using Machine Learning and Kubernetes Logs to Automate Security Threat Detection

Source:-https://containerjournal.com Kubernetes is quickly consolidating its place as the leading container orchestration platform for cloud-native applications, with adoption at 59% among enterprise IT professionals as of March. But while Kubernetes delivers agility, flexibility and scalability for DevOps teams, it also creates complexity that can be an enigma for SecOps teams—especially when something goes wrong. When it comes to detecting threats and tracking down breaches in Kubernetes, security teams’ key asset is the Kubernetes API server audit log. The audit log

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DevOps Leave a comment

Adopting DevOps for legacy systems

Source: itproportal.com. Organisations have a mix of legacy and advanced systems. Regardless of the race towards digitalisation, it is impossible to rip and replace legacy systems, entirely. Primary blocker is the dependency on the system status quo to generate revenue and difficulty of making changes on a running system. Next hurdle is the lost knowledge of systems and applications because people who built them moved on. But legacy systems should not prevent enterprises from competing in the digital age. The legacy

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Continuous Integration, DevOps, DevOps Best Practices, DevOps Updates Leave a comment

4 considerations for Machine Learning System in Production

Source:- infoworld.com Writing a recommendation engine isn’t easy, but at least it’s straightforward. Given a large data set and some symptoms, you can determine what disease a patient might have. The problem is getting that recommendation engine, written in R or Python, and integrating it with an existing medical records system written in a more traditional language and delivered over the web. Most of the attempts to do this look a bit like a wonderful mosaic quilt … with a

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