Bringing The Future of DevOps to the Indian IT Industry

Source – entrepreneur.com In the ever-changing world of technology, the need to be agile is crucial. Businesses are innovating at a fast pace to keep up with the demands of their consumers. In times of such rapid innovation, the need to have successful and speedy software development is of primary importance. And here’s where DevOps comes into the picture. DevOps brings together software development and software operation for faster processes; it prioritises the organizational changes required for the same. Bringing together

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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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SnapLogic Extends DevOps Reach via GitHub

Source – devops.com SnapLogic has extended the reach of its SnapLogic Enterprise Integration Cloud into the realm of DevOps by making it possible to store integrations creating using Snap connectors in GitHub. The company also announced integration with the DC/OS platform from Mesosphere along with a new service catalog and several updates to the Iris artificial intelligence (AI) software it uses make recommendations concerning the construction of integration pipelines. At the same time, SnapLogic has enhanced both its monitoring tools for application programming

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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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Digital Transformation Strategy? Think Cloud

Source – devops.com A few years back companies used innovation and digital transformation mostly to differentiate themselves and to stay competitive. The drastic growth in digital and cloud computing over the last couple of years have changed this mindset. Today, organizations have to be innovative and leverage the latest technologies just to sustain and stay in business. Enterprises that implement online retail business, online banking and several other online services aren’t considering those channels simply as another route to increase their

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Best Practices for Application Security Testing in the Era of DevOps and AI

Source – devops.com Application security testing is no easy feat. And yet, it’s usually the first topic that most articles about application security address. The reasons are simple: As the pace of application development techniques (and their inevitable vulnerabilities) evolve, AppSec personnel have found themselves caught between the desire to keep pace with their management of security testing requirements and their ability to allow the developer teams to operate in the modern, fast-paced ecosystem of DevOps and artificial intelligence. To better

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DevOps Improving Outcomes through Improved Business Processes

Source – digitallearning.eletsonline.com There has been a lot talked about artificial intelligence and automation however, not much has been written about DevOps, which is a software engineering culture and practice that aims at unifying software development (Dev) and software operation (Ops), says Anand Sahay, CEO, Xebia, in conversation with Elets News Network (ENN). Why is there a need for courses in DevOps? Digital has completely changed the way people want to do business today. It is forcing businesses to adopt digital technologies and

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