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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DevOps engineers think Docker, Ansible and Kubernetes are the top 3 tools to learn

Source – jaxenter.com The data is in. According to Packt’s third annual Skill Up survey, Machine Learning, Big Data, and cloud computing are the top three trends in tech for 2017. Five thousand developers and tech professionals across the world responded to Packt’s third annual Skill Up survey to share their thoughts on the latest tech tools and trends, and how they work and learn. This year’s survey went even deeper than previous years, asking respondents to share their opinions on

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7 Machine Learning Tools for IIoT

Source – edgylabs.com Companies at the forefront of the machine learning field offer open source libraries of solutions for companies and the average person. Below is a list of seven open-source platforms that help businesses integrate machine learning into their production process. With these toolkits, businesses, regardless of their size, can get access to the same ML resources developed and used by prestigious companies. The 7 Machine Learning Tools for IIoT: 1. Amazon Machine Learning: In 2015, Amazon’s subsidiary AWS (Amazon Web

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4 Thing Machine-Learning Algorithms Can Do for DevOps

Source – insidebigdata.com As the IT industry struggles to elevate performance, companies are looking to DevOps to deliver on the promise of a newly efficient process that includes frequent release cycles to feed the higher demanding consumer. However, speeding up release cycles is far easier said, than done. IT practitioners are constantly seeking new tools to improve their responsiveness to business needs in this new environment. Transforming to a digital environment can prove to be a difficult evolution for most enterprise

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