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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DevOps 101: Adopt Continuous Innovation

Source – informationweek.com DevOps requires some hard work and tough choices, but in the end can keep a business competitive and innovative. I’ve spent a good portion of my career interacting with DevOps, but from an infrastructure and data center perspective. In that role, my goal was to eliminate legacy components from infrastructure so that the entire process could then impact both DevOps and the business overall. Today, my focus has shifted into the world of cloud, DevOps, and advanced technology

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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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How Artificial Intelligence, Machine Learning Can Help DevOps

Source – devops.com Artificial intelligence (AI) and machine learning (ML) can help the humans in DevOps break free from focusing on simple activities. One aspect of DevOps is automating routine and repeatable actions, and AI and ML can perform these activities with enhanced efficiency to improve the performance of teams and business. There are algorithms that can perform many operations and procedures, allowing those in DevOps to execute their part effectively. This article discusses how DevOps engineers can use AI and

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