Work is dead, welcome to workflows

Source:- techhq.com Most contemporary pieces in the media and wider technical press that attempt to highlight the current focus on workflows and the technologies that support them start with a reference to the Wikipedia definition. So let’s not buck that trend. According to free encyclopedia’s current description, “A workflow consists of an orchestrated and repeatable pattern of business activity enabled by the systematic organization of resources into processes that transform materials, provide services, or process information.” In other words, workflows define jobs.

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The Basics of CI/CD for Data Science and Machine Learning

Source:- informationweek.com Continuous integration and continuous deployment are IT practices that encourage testing code often. Learn how these practices also shape data-driven initiatives. The basics behind how machine learning and data science should work often feel less than basic. Machine learning practitioners from programmers to scientists are learning how to apply advanced statistics and mathematical application within the context of software programming. The result is complexity in selecting good machine learning models that conflict with management’s options at hand, be it

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The New AI Overseer Role in DevOps

Source- devops.com Robot overlords are the stuff of science fiction fantasy. Today, with more teams implementing artificial intelligence (AI), DevOps teams need a hard, continuous look at reality. An AI overseer watching apps at work could make the difference between sadness and euphoria in your efforts. We’re seeing AI misses more and more often. Facebook recently admitted its algorithms failed to stop uploads of a particularly heinous live-streamed video. Autonomous vehicles have struck cars and people. Jaywalkers in China have been

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Step by step, how enterprises should embark on the journey to the cloud

Source- siliconangle.com Cloud computing has become the principal paradigm for enterprise applications. As businesses modernize their computing and networking architectures, cloud-native architectures are the principal target environments. As a result, enterprise deployment of all-encompassing cloud computing is accelerating. Many enterprises have embarked on a journey to computing in and across a multiplicity of clouds. These are the chief steps on this journey: Zero in on core cloud use cases The first step on the cloud journey is to identify the chief use

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How AIOps Helps You Get More Out of DevOps

Source- devops.com DevOps may seem like an overused term. It involves taking an application’s source code and running it in an environment. It can cover the processes, the technology or even the people that maintain the technology that is running those very processes. At its heart, though, DevOps is about helping developers to be self-sufficient when it comes to the basic operations around getting their application to a real environment. Once things are running in a test or production environment, there

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What is the Next of Virtualization

Source- virtualization.cioreview.com It has been more than half a century in the history of virtualization since IBM’s first effort to virtualize mainframe in the mid-1960s. Multiple applications could run at once, and hardware utilization and productivity were increased. The evolution of virtualization has been accelerated in the past 20 years. The resources such as a server, desktop, storage, file system, operating system and, networking are virtualized, and workloads are managed and run over virtual resources to achieve scalability and elasticity. Virtualization

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THE LAST MILE FOR ANALYTICS

Source- analyticsinsight.net The promise of machine learning (ML) is vast and investment is increasing, but the results are lagging because too many projects don’t make it to production in an operational system. A Wall Street data scientist recently said to me, “It took us two months to build and train the model and six months to deploy it.” This is the norm. Analytics leaders cite deployment and productionization – aka the “last mile” – as the biggest bottleneck for analytics and ML

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Making enterprise automation your app development advantage

Source- enterpriseinnovation.net In today’s agile, innovation-driven and dynamic market, automation rules! It allows companies to streamline, optimize, eliminate human error and reallocate valuable human resources to other value-adding tasks. Automation provides the foundation for DevOps, which brings together the two separate worlds of app development and software operations. In theory, automation can improve rollout of app releases, increase deployment speed, lower failure rates, shorten the time between fixes, and reduce mean time to recovery when apps crash. The reality, however, can

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Machine learning now the top skill sought by developers

Source- zdnet.com Developers want to learn the data sciences. They see machine learning and data science as the most important skill they need to learn in the year ahead. Accordingly, Python is becoming the language of choice for developers getting into the data science space. Those are some of the takeaways from a recent survey of more than 20,500 developers conducted by SlashData. The survey shows data science and machine learning to be the top skill to learn in 2019. These will be

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DevOps & machine learning improves app performance!

Source – devopsonline.co.uk Machine learning has been hyped (and in some cases overhyped). However, one successful DevOps use case for machine learning is application performance management. Why? Simply put, machine learning is needed to analyse the volume, velocity, and variety of big data generated by today’s dynamic application environments. By applying machine learning to identify patterns and anomalies, DevOps teams can better troubleshoot intermittent and complex problems, understand usage patterns, reduce bug rates, and improve the customer experience. What’s changed? Modern applications based on

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