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AWS Unfurls Bevy of Automation Tools to Streamline DevOps

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At the AWS re:Invent conference, Amazon Web Services (AWS) added a bevy of tools to its portfolio intended to accelerate the pace of application development while simultaneously simplifying DevOps processes. AWS development tools unveiled this week include AWS Amplify Studio, a visual development environment that allows developers to create web application user interfaces (UIs) with minimal coding. Ken Exner, head of product for developer tools at AWS, said as an extension of an existing AWS Amplify Studio tool, this latest addition allows developers to customize application UIs at a higher level of abstraction using a library of components while enabling them to drop down to a lower level of coding to customize their application further whenever required. After the UI is designed, AWS Amplify Studio automatically generates the associated JavaScript or TypeScript code for the developer. In general, AWS is committed to improving developer productivity by providing, for example, automated reasoning tools that automate processes without locking devs into a layer of abstraction that, ultimately, may create a wall that blocks them from meeting customization requirements, he said.


7 DevOps skills for Machine Learning Operations

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MLOps has been a hot topic in 2021, with many people talking about and companies aiming at implementing it. The reason is clear: MLOps enables the application of agile principles to machine learning projects, which means shorter release cycles and higher quality standards. From the technology standpoint, I would say the main pieces for successful MLOps implementation are available: the ability to train and serve ML models using containers, plenty of data pipeline orchestration tools, automated testing frameworks, and mature DevOps practices. Having the technology pieces in hand does not mean success, though. Building MLOps teams is challenging due to the roles typically involved: Data Scientists, Machine Learning Engineers, Data Engineers, DevOps Engineers, and management staff. Experience shows that people in these roles do not necessarily speak the same language and, from my point of view, someone should take the responsibility of connecting the dots.


Global Machine Learning Markets Report 2021: The New Driving Force for DevOps - ResearchAndMarkets.com

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DUBLIN--(BUSINESS WIRE)--The "Machine Learning: The New Driving Force for DevOps" report has been added to ResearchAndMarkets.com's offering. When they work together, software development and operations teams can advance a company's business transformation. The integration of these teams, also known as DevOps, streamlines the legacy software development process. However, with the growing emphasis on digital transformation, the pace of development and innovation has increased. Therefore, the need for optimal orchestration in DevOps is rising, which requires innovation and advanced tools and technologies.


7 DevOps skills for Machine Learning Operations

#artificialintelligence

MLOps has been a hot topic in 2021, with many people talking about and companies aiming at implementing it. The reason is clear: MLOps enables the application of agile principles to machine learning projects, which means shorter release cycles and higher quality standards. From the technology standpoint, I would say the main pieces for successful MLOps implementation are available: the ability to train and serve ML models using containers, plenty of data pipeline orchestration tools, automated testing frameworks, and mature DevOps practices. Having the technology pieces in hand does not mean success, though. Building MLOps teams is challenging due to the roles typically involved: Data Scientists, Machine Learning Engineers, Data Engineers, DevOps Engineers, and management staff. Experience shows that people in these roles do not necessarily speak the same language and, from my point of view, someone should take the responsibility of connecting the dots.


Artificial intelligence to play a more important role within DevOps - DevOps Online

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A new study by GitLab found out that Artificial Intelligence (AI) and Machine Learning (ML) are starting to play an important role within DevOps. Indeed, it was reported that many enterprises are releasing code ten times faster than in previous surveys, with 84% of developers and managers stating they're releasing code faster than before. Besides, 57% declared that code is being released twice as fast. The study thus showed that 75% of enterprises are using AI/ML to test and review their code before release, while 25% use full test automation. It was noted that this acceleration is due to the addition of source code management, CI, and CD to DevOps practices. Moreover, almost 12% of respondents said that adding a DevOps platform has sped up the process, and 10% said the same about adding automated testing.


Global Big Data Conference

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Unlike DevOps, MLOps is much more experimental in nature. Data scientists try different features, parameters, models. In all these iterations, they must manage the code base and create reproducible results.


A Close Look at Application Solution Architecture

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An application architecture describes the patterns and techniques used to design and build an application. The architecture gives you a roadmap and best practices to follow when building an application so that you end up with a well-structured app. Application Architecture depicts different architecture aspects such as Functional Analysis, Implementation Architecture, Tools & Technology, Data, Non-Functional, Deployment Architecture, views of an Application. It enables you to envision the big picture and reduce cost by removing redundancies. Integrating components in the application and other systems are also clearly demarcated for everyone to visualize.


IBM launches client innovation centre in Mysuru - Express Computer

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The Karnataka Digital Economy Mission (KDEM), as part of its Spoke-shore Strategy' to attract companies to set up operations in cities'Beyond Bengaluru', fuelled the launch of IBM's Client Innovation Centre (CIC) in Mysuru today. The CIC initiative aims at supporting the rapid, high-tech driven economic growth in tier 2 and 3 regions while providing comprehensive hybrid cloud and AI technology consulting capabilities. The CIC specializes in design, software engineering and analytics. Congratulating KDEM and IBM on the launch of their Client Innovation Centre, Dr. Ashwath Narayana, Minister for Higher Education; IT & BT, Science & Technology; Skill Development, Entrepreneurship & Livelihood said, "I applaud KDEM for making the Spoke-shore strategy a reality in India and wholeheartedly welcome IBM's CIC to Mysuru. It is encouraging to see that the industry is recognising places such as Mysuru, thereby echoing our vision of Beyond Bengaluru. We are confident that this partnership will propel Karnataka to become a frontrunner for digital services and technologies world over. B.V. Naidu, Chairman, Karnataka Digital Economy Mission said, "The launch of the Client Innovation Centre at Mysuru resonates with KDEM's Spoke-shore initiative of attracting at least 100 GCCs by 2025.


Top 10 Key Difference Between MLOps And DevOps

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Machine learning is a term nearly everybody in the IT space has heard at this point--but it is not just a popular expression utilized in flashy presentations any longer. As machine learning has begun to turn out to be more applied and less hypothetical, the business has started to join it into significant tasks. Both MLOps and DevOps mean to put a piece of programming in a repeatable and shortcoming lenient work process, but in MLOps, the software also has a machine learning component. DevOps is a set of practices that plans to abbreviate a framework's advancement life cycle and give nonstop conveyance high programming quality. Similarly, MLOps is the method involved with automating and productionalizing machine learning applications and work processes.


MLOps vs. DevOps: Why data makes it different

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Much has been written about struggles of deploying machine learning projects to production. As with many burgeoning fields and disciplines, we don't yet have a shared canonical infrastructure stack or best practices for developing and deploying data-intensive applications. This is both frustrating for companies that would prefer making ML an ordinary, fuss-free value-generating function like software engineering, as well as exciting for vendors who see the opportunity to create buzz around a new category of enterprise software. The new category is often called MLOps. While there isn't an authoritative definition for the term, it shares its ethos with its predecessor, the DevOps movement in software engineering: By adopting well-defined processes, modern tooling, and automated workflows, we can streamline the process of moving from development to robust production deployments.