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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