Efficient MLOps in a Kubernetes Environment

Source:-containerjournal.com MLOps addresses the specific needs of data science and ML engineering teams without impacting Kubernetes If your organization has already started getting into machine learning, you will certainly relate to the following. If your organization is taking its first steps into data science, the following will illustrate what is about to be dropped on you. If none of the above strikes a chord this might interest you nonetheless, because AI is the new frontier and it won’t be long

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Opening The Door To Innovation With Data Superpowers

Source:-forbes.com Helping your data teams do their jobs means empowering them to work efficiently and without barriers. This article looks at how AI and ML technologies can empower your teams to help your business grow, and how to make modern data analytics work for you. Last week, I celebrated my first anniversary as general manager and VP of engineering for our data analytics platform here at Google Cloud. Despite all the unprecedented experiences we’ve had this year, we’re thankful to

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Google Cloud hooks up dev tools in Looker

Source:-computerweekly.com This term was meant to express the existence of a software estate sat on a variety of hardware resources with a selection of analytics tools, network topology controls and data channels. People still talk about the IT stack, but now, increasingly, we’re talking about the ‘data infrastructure’ that exists inside any given organisation — so much so that water cooler chats now circle around the level of ‘data infrastructure investment’ being carried out inside the business. In light of

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Algorithmia Looks to Meld MLOps and DevOps

Source:-devops Algorithmia, in the latest update to the enterprise edition of its namesake machine learning operations (MLOps) platform, is enabling software development lifecycle practices to be applied to the building of algorithms by making it possible to debug them using desktop tools that are widely employed. The enterprise edition of Algorithmia will enable users to write and run local tests for algorithms as shared local data files. Desktop developer tools that can now integrate with that process include PyCharm, Jupyter

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Google Cloud Delivers Enhancements To Looker That Optimize Performance And Accelerate Application Development

Source:-aithority.com Looker, the business intelligence (BI) and analytics platform that is now part of Google Cloud, is helping customers augment their data experiences with enhancements in three key areas: modern business intelligence (BI), foundational optimization and performance improvements, and updated data and development tools. Included in this release is support for the complete Google Marketing Platform, significant upgrades for application builders, and an update to Marketplace where customers can access and share newly built data experiences. “Looker is continuously evolving

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Udacity, Microsoft to skill people in machine learning for Azure Cloud

Source:-outlookindia.com New Delhi, June 12 (IANS) US-based learning platform Udacity on Friday announced a partnership with Microsoft to confer scholarships for a new Machine Learning Nanodegree programme in Microsoft Azure. Phase one of the programme will provide 10,000 applicants access to the two-month-long foundation course titled ”Introduction to Machine Learning on Azure” with a low-code experience. The second phase will offer a scholarship for the new programme in Microsoft Azure to top 300 performers of the foundation course, Udacity said

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How DevOps Powered by AI and Machine Learning Is Delivering Business Transformation

Source:-devops.com The use of artificial intelligence (AI) and machine learning (ML) is fundamentally changing the way we think about DevOps. Most notably, it is delivering a new form of DevOps that recognizes the need to have systems that are intelligent by design and underpinned by comprehensive security (DevSecOps). For many, this will be the crucial next step if DevOps is to shorten the software development lifecycle for all connected intelligent systems, ensuring the continuous delivery of secure high-quality software. By

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Azure-hosted AI for finding code defects emitted – but does it work?

Source:-theregister.co.uk How many defects has it found? Never mind that, check out the architecture flow chart Altran – in association with Microsoft – has pushed out an open source project to find code defects via AI whenever you commit code. It is easy to see why Microsoft is keen. This is a self-managed service where you download the code and purchase Azure resources on which to host it. The project from Altran – a consulting and software engineering firm that’s

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The pros and cons of AI and ML in DevOps

Source:-information-age.com AI and ML are now common within most digital processes, but they bring faults as well as benefits when it comes to DevOps The pros and cons of AI and ML in DevOps image AI and ML can be very beneficial, but they aren’t without their faults. Machine learning (ML), and artificial intelligence (AI) in general, has been commonly used within DevOps to aid developers and engineers with tasks. The technology is highly capable of speeding tasks up and

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3 test design principles to get you to continuous integration

Source:-techbeacon.com If your test case is causing more harm than good, is it truly useful? In the days of legacy software delivery, with long lead times and great difficulty changing the product once shipped, nearly all test cases (automated or not) were good test cases. In this era of continuous delivery, though, this calculus has shifted. It’s shockingly easy to end up with a test that inadvertently causes your software to be less stable—whether that’s by building false confidence in

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Solving the problems of separate admins and developer teams around Salesforce DevOps.

Source:-enterprisetimes.co.uk The scope and complexity of most enterprise Salesforce projects has grown to a scale that they can no longer be managed efficiently without a mature deployment, testing and collaboration process. This demands a new kind of partnership and understanding between admins and developers. But there are potential bumps in the road. All organisations and businesses rely ever more on digital systems. They must increasingly trust the teams that maintain them to deliver innovation without risking downtime. Salesforce has become

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What’s Next in DevOps?

Source:-infoq.com Key Takeaways The shift from project to product continues, with an eye towards long-term maintainability and adaptability. The shift to small, semi-autonomous teams continues, as does the market for platforms that integrate and aggregate information from disparate tools. The growing importance of data science and analytics on the development lifecycle. Concepts such as Value Stream Mapping that look at the entire Dev lifecycle become increasingly important, and pre-built tools to support this become more common. Increasingly specialized vendors and

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Google Announces Cloud AI Platform Pipelines to Simplify Machine Learning Development

Source:-infoq.com In a recent blog post, Google announced the beta of Cloud AI Platform Pipelines, which provides users with a way to deploy robust, repeatable machine learning pipelines along with monitoring, auditing, version tracking, and reproducibility. With Cloud AI Pipelines, Google can help organizations adopt the practice of Machine Learning Operations, also known as MLOps – a term for applying DevOps practices to help users automate, manage, and audit ML workflows. Typically, these practices involve data preparation and analysis, training,

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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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