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CI/CD bridges the gaps between development and operation activities and teams by enforcing automation in building, testing and deployment of applications. CI/CD services compile the incremental code changes made by developers, then link and package them into software deliverables. [ 3 ]
Continuous deployment (CD) is a software engineering approach in which software functionalities are delivered frequently and through automated deployments. [ 1 ] [ 2 ] [ 3 ]
This is an accepted version of this page This is the latest accepted revision, reviewed on 5 January 2025. Set of software development practices 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. DevOps is complementary to ...
The pipeline skeleton is especially useful when the team's migration to CD requires a large effort and mindset changes over a long period of time. Expert drop: Assign a CD expert to join tough projects as a senior member of the development team. Having the expert on the team helps to build the motivation and momentum to move to CD from inside ...
Continuous Integration: Improving Software Quality and Reducing Risk. ISBN 9780321630148. {}: CS1 maint: multiple names: authors list ; Ching, Maria Odea; Porter, Brett (2009-09-15). Apache Maven 2 Effective Implementation: Build and Manage Applications with Maven, Continuum, and Archiva. Packt Publishing Ltd. ISBN 9781847194558.
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]
The earliest known work (1989) on continuous integration was the Infuse environment developed by G. E. Kaiser, D. E. Perry, and W. M. Schell. [4]In 1994, Grady Booch used the phrase continuous integration in Object-Oriented Analysis and Design with Applications (2nd edition) [5] to explain how, when developing using micro processes, "internal releases represent a sort of continuous integration ...
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 ...