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DevOps is a methodology integrating and automating the work of software development (Dev) and information technology operations (Ops). It serves as a means for improving and shortening the systems development life cycle. [1] DevOps is complementary to agile software development; several DevOps aspects came from the agile approach.
As DevOps is a set of practices that emphasizes the collaboration and communication of both software developers and other information technology (IT) professionals, while automating the process of software delivery and infrastructure changes, its implementation can include the definition of the series of tools used at various stages of the ...
Modern-day DevOps practices involve: continuous development, continuous testing, continuous integration, continuous deployment, and; continuous monitoring; of software applications throughout its development life cycle. The CI/CD practice, or CI/CD pipeline, forms the backbone of modern day DevOps operations.
DevOps focuses on continuous delivery by leveraging on-demand IT resources and by automating test and deployment of software. This merging of software development and IT operations has improved velocity, quality, predictability and scale of software engineering and deployment. Borrowing methods from DevOps, DataOps seeks to bring these same ...
CCA grew out of a push to develop more reliable software faster. [1] Gartner describes CCA as “Embodying lean, agile and collaborative concepts core to DevOps initiatives, CCA tools bring a newly found level of precision, efficiency and flexibility to the challenges of infrastructure and application configuration management.” [4]
DevOps is a broader, more inclusive concept that includes software feature planning, code development, testing, deployment, configuration, monitoring, and feedback. It was an attempt to integrate some of the disconnected toolchains. Testing is included in the broader DevOps methodology. TestOps [12] is not simply providing additional emphasis ...
DevOps is a software engineering approach that centers around cultural change, specifically the collaboration of the various teams involved in software delivery (developers, operations, quality assurance, management, etc.), as well as automating the processes in software delivery. [23] [24] [25]
MLOps is the set of practices at the intersection of Machine Learning, DevOps and Data Engineering. MLOps or ML Ops is a paradigm that aims to deploy and maintain machine learning models in production reliably and efficiently. The word is a compound of "machine learning" and the continuous delivery practice (CI/CD) of DevOps in the software ...
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