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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8 ways Artificial Intelligence can improve DevOps

Source – hub.packtpub.com DevOps combines development and operations in an agile manner. ITOps refers to network infrastructure, computer operations, and device management. AIOps is artificial intelligence applied to ITOps, a term coined by Gartner. Makes us wonder what AI applied to DevOps would look like. Currently, there are some problem areas in DevOps that mainly revolve around data. Namely, accessing the large pool of data, taking actions on it, managing alerts etc. Moreover, there are errors caused by human intervention. AI works heavily with data and can help improve DevOps in numerous

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OverOps Brings Machine Learning to DevOps

Source – devops.com OverOps has launched a namesake platform employing machine learning algorithms to capture data from an IT environment that identify potential issues before a DevOps team decides to promote an application into production. Company CTO Tal Weiss said the OverOps Platform is unique in that, rather than relying on log data, it combines static and dynamic analysis of code as it executes to detect issue. That data then can be accessed either via dashboards or shared with other tools via an

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Applying machine learning to DevOps

Source – jaxenter.com DevOps methodologies are rapidly increasing and generating vast and diverse data sets across the life cycle of entire application including development, deployment, and performance management. Only a robust analysis and monitoring layer can particularly harness this data for the ultimate DevOps goal that is end-to-end automation. The rise of machine learning and its related capabilities, such as artificial intelligence and predictive analytics, has pushed organizations to explore implementing new analysis models that mainly rely on mathematical algorithms.

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