When AI meets DevOps: Getting the best out of both worlds

Source – cloudcomputing-news.net DevOps has been widely embraced by businesses under pressure to get competitively advantageous digital deliverables to market at the fastest possible cadence—especially given the reality of limited coder headcount and the need to rigorously avoid brand-toxic snafus in the customer experience. Artificial intelligence (AI), in stark contrast, is a potentially transformative digital discipline that is still very new to most enterprise IT organizations. But while it’s certainly important that CIOs nurture AI adoption with appropriately resourced pilots, it’s

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The Next Generation of DevOps: ML Ops

Source – insidebigdata.com In this special guest feature, Debashis Saha, Vice President of Platform Engineering at Intuit, discusses how DevOps methodologies can be applied to machine learning, in what he calls “ML Ops.” With ML Ops, he believes it can provide an end-to-end automation of the process, creating transparency and delivering efficiency and productivity for everyoenbody involved to deliver value rapidly. As VP of Platform Engineering for Intuit, Debashis leads the engineering teams responsible for the platform, developer and application services that

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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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4 experts predict what’s coming in 2018: DevOps, AI, and more

Source – jaxenter.com Predicting the future is a hard gig. For every right guess, there’s always an over-excited promise that falls short of reality. However, we’re taking a lot of the guesswork out of predicting what’s hot in 2018 by asking the experts what they think. While nothing is set in stone, it looks like 2018 is going to bring a lot of growth for DevOps, cloud technologies, and data science. However, signs are unclear for what will happen to artificial intelligence and

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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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Why Python is a crucial part of the DevOps toolchain

Source – jaxenter.com DevOps is a way of thinking; it’s an approach, not a specific set of tools. And that’s all well and good – but it only gives you half the picture. If we overstate DevOps as a philosophy or a methodology, then it becomes too easy to forget that the toolchain is everything when it comes to DevOps. In fact, DevOps thinking forces you to think about your toolchain more than ever – when infrastructure becomes code, the way

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Why big data needs DevOps ?

Source:- globalbigdataconference.com Many big data analytics teams choose to not use DevOps methodologies, but there are real benefits to applying DevOps concepts to those big data initiatives. Extracting accurate and meaningful answers from big data is tough. It’s often made more challenging given the way big data software developers and IT operations lack coordination in many enterprises. Even though an IT organization may practice sound DevOps strategies for other supported applications, big data projects often remain siloed for a variety

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