Best DevOps

AppDynamics joins the dots in Cisco’s roadmap to multicloud

Source:-siliconangle.com It’s taken just a couple of decades for humanity to develop a digital reflex. The consulting of connected devices has become second nature, and software applications are now the primary interface between a business and its customers. This means app performance has become business-critical. Customers demand speed, accuracy and accessibility. And apps are the primary source of the digital era’s most precious resource — data. When apps don’t work, customers go elsewhere. Fast. And when customers move to a

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Google Announces Beta Launch of Cloud AI Platform Pipelines

Source:-infoq.com Google Cloud Platform (GCP) recently announced the beta launch of Cloud AI Platform Pipelines, a new product for automating and managing machine learning (ML) workflows, which leverages the open-source technologies TensorFlow Extended (TFX) and Kubeflow Pipelines (KFP). In a recent blog post, product manager Anusha Ramesh and developer advocate Amy Unruh gave an overview of the offering and its features. Cloud AI Platform Pipelines addresses the problem of managing end-to-end ML workflows, which span the lifecycle from ingesting raw

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Containers and Kubernetes: 3 transformational success stories

Source:-sg.channelasia.tech Powerful combo of workload portability and orchestration has become an invaluable business asset in multi-cloud and hybrid cloud environments Companies across industries are pushing to move data and workloads to the cloud, whether as part of digital transformations or to avoid building costly new infrastructure to handle growing demand. For many organisations, key to this move are containers and Kubernetes — especially when multiple cloud services are involved. Containers are standalone software packages that bundle together all of an

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The 7-Step plan for successful AIOps implementations

Source:-softwaretestingnews.co.uk The idea of applying artificial intelligence and machine learning to more rapidly and accurately resolve IT incidents and manage alerts has been gaining steam in the past year. While AIOps, as it’s frequently called, has spawned an entirely new market of startups, many enterprise IT leaders are playing a cautious hand so far – and for good reason. There are risks, though. If an AIOps tool goes wrong out of the gates, IT and executive trust diminishes. That’s why

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New Google Cloud Feature Simplifies Data Science Deployments

Source:-cdotrends.com Google Cloud this week announced a new feature that can make it significantly easier for system administrators and data scientists to set up and maintain their specialized data infrastructure environments in the cloud. Called “machine images”, the new feature essentially stores all the information needed to restore a virtual machine. While this can already be done by an older feature known as “custom images”, machine images can span multiple disks and contains instance properties of individual machines, instance metadata,

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What would machine learning look like if you mixed in DevOps? Wonder no more, we lift the lid on MLOps

Source:-theregister.co.uk And why do such a thing? Well, how else will you push your artificially intelligent software into production? Achieving production-level governance with machine-learning projects currently presents unique challenges. A new space of tools and practices is emerging under the name MLOps. The space is analogous to DevOps but tailored to the practices and workflows of machine learning. Why MLOps is Needed Machine learning models make predictions for new data based on the data they have been trained on. Managing

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The Pros and Cons of Google’s New AI Transparency Tools

Source:-insidebigdata.com In this special guest feature, Jay Budzik, CTO at ZestFinance, discusses the set of developer tools that Google launched that allow data scientists to create explainable ML models, and also the reality of these new tools and how they are likely to impact the financial services market. ZestFinance is a software company that helps banks and lenders build, run, and monitor fully explainable machine learning underwriting models. As CTO, Jay oversees Zest’s product and engineering teams. His passion for

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Simplifying Conversational AI, One Interaction At A Time

Source:-forbes.com What if we could speak with our devices, cars, and homes just as easily as we do with our friends? Conversation is the bedrock of human communication, a transformative tool that reveals what’s inside our heads and hearts. Voice is our primary means of connecting with others—and, increasingly, it’s how we want to engage with the machines around us, too. Thanks to advances in speech recognition, artificial intelligence, neural networks, and processing power, we can tap into the capabilities

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GitHub Releases ML-Based “Good First Issues” Recommendations

Source:-infoq.com GitHub shipped an updated version of good first issues feature that uses a combination of both a machine learning (ML) model that identifies “easy” issues and a hand curated list of issues that have been labeled “easy” by project maintainers. New and seasoned open source contributors can use this feature to find and tackle easy issues in a project. In order to eliminate the challenging and tedious task of labelling and building a training set for a supervised ML

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From DevOps to MLOps: The evolution of DevOps

Source:-itproportal.com Luca Ravazzolo explains how the evolution of DevOps is likely to continue, with potential far beyond its current form. The introduction of DevOps has had a profound effect on developers and the IT industry, completely changing mindsets with concepts like continuous integration and continuous delivery now much more commonplace. Over the last few years, DevOps has matured, becoming more mainstream and widely adopted, and this has led to a gradual evolution of the approach. During this time, DevOps has

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