Seven steps to move a DevOps team into the ML and AI world

Source – techtarget.com Nobody questions the potential for machine learning and artificial intelligence to revolutionize DevOps productivity and business efficiency as a whole, really. An invisible hand, however, keeps organizations from seeing ML and AI to their full potential. There are seven basic rules to implement ML and AI in real-life DevOps situations. Done right, ML/AI and DevOps can turn enterprises into digital attackers that release higher-quality software faster and at lower cost. To release on time and within budget, there

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How AI will influence the world of cloud-based collaboration and conferencing

Source – cloudcomputing-news.net Those who attended Cloud Expo Europe earlier this week took their opportunity to assess the next level of cloud services, ranging from blockchain, to artificial intelligence (AI) and machine learning. The cloud underpins these technologies and enables them to flourish, while as this publication has previously reported, the M&A cycle has been lit up by it. But what are some of the practical applications for cloud-enabled AI? Lifesize, a cloud-based conferencing hardware and software provider, is exploring how

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Emerging Data Center Trends: From DevOps To DataOps

Source – forbes.com If asked to list the top trends that are shaping the enterprise data center today, most technologists and tech investors would likely agree on a core set. The list would include technologies like such as cloud computing, containers and virtualization, microservices, machine learning and data science, flash memory, edge computing, NVMe and GPUs. These technologies are all important for organizations pushing digital transformation. The harder question: What’s coming next? Which emerging technologies or paradigm shifts are poised to

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How technology changes the rules for doing agile

Source – enterprisersproject.com More companies are trying agile and DevOps for a clear reason: Businesses want more speed and more experiments – which lead to innovations and competitive advantage. DevOps helps you gain that speed. But doing DevOps in a small group or startup and doing it at scale are two very different things. Any of us who’ve worked in a cross-functional group of 10 people, come up with a great solution to a problem, and then tried to apply the

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IT ops pros predict routes to DevOps efficiency in 2018

Source – techtarget.com IT operations is a fast-moving river of change. It might bend or fork as it travels, you might go over a Waterfall, but it never doubles back. Expert IT professionals and analysts –– many of whom write tips on the tools and technologies you use every day — lay out their predictions for where IT operations is heading in 2018. Everyone expects a heavier emphasis on DevOps efficiency through smarter adoption and evolved tools and practices. Another broad

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How to work through the four complicated DevOps stages

Source – techtarget.com For thousands of years, we have been devising ways to cross bodies of water. Historically, our attempts were crude — makeshift rafts and rickety, ferrylike vessels. So, we started building bridges, albeit basic ones. Ferries and bridges still need upkeep as weather and increasing traffic require intelligent decision-making and technology to optimize transit. Let’s apply this to DevOps. Like bridging two shorelines together, DevOps involves efficiently linking an organization’s business idea to its market. For an organization to

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5 reasons DevOps will be a big deal in the year ahead

Source – infosurhoy.com The typical enterprise has become a nonstop software and data factory, operating 24×7. Technology has to be there at all times. DevOps has been a hot topic and pursuit for a few years now. During this time, many enterprises have been attempting to put this way of working — in which development output is aligned with ongoing business requirements — in place in one form or another. But 2018 promises to be the year when it all really

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DevOps Automation report — it was always about DevOps

Source – gigaom.com One has to wonder what was going through Barry Boehm’s head when, back in 1986, he formulated what he called the ’Spiral Model’ of software development, which brought the notion of iteration into the process of delivering software to the masses. He undoubtedly wasn’t the first to employ faster development cycles to solve software problems; however, he was key in presenting such approaches as a viable alternative to exhaustive, long-winded models such as ‘Waterfall’. General acceptance of ‘fast’

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What is DataOps? Collaborative, cross-functional analytics

Source – cio.com.au What is DataOps? DataOps (data operations) is an emerging discipline that brings together DevOps teams with data engineer and data scientist roles to provide the tools, processes and organizational structures to support the data-focused enterprise. “You’ve got the modern trend for development of DevOps, but more and more people are injecting some sort of data science capability into development, into systems, so you need someone on the DevOps team who has a data frame of mind,” says Ted

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Box introduces framework to apply machine learning to cloud content

Source – cloudcomputing-news.net Cloud content management company Box has unveiled Box Skills, a framework for applying machine learning tools such as computer vision, video indexing, and sentiment analysis to stored content. Box Skills will facilitate businesses to re-imagine the business processes considered as impractical to digitise or automate or too expensive. At BoxWorks 2017, Box previewed three initial Box Skills currently in development, leveraging machine learning tools from IBM Watson, Microsoft Azure and Google Cloud to solve common business use cases,

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