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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 ...
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.
Agile software development is an umbrella term for approaches to developing software that reflect the values and principles agreed upon by The Agile Alliance, a group of 17 software practitioners, in 2001. [1] As documented in their Manifesto for Agile Software Development the practitioners value: [2] Individuals and interactions over processes ...
The Microsoft Security Development Lifecycle (SDL) is the approach Microsoft uses to integrate security into DevOps processes (sometimes called a DevSecOps approach). You can use this SDL guidance and documentation to adapt this approach and practices to your organization.
The scaled agile framework (SAFe) is a set of organization and workflow patterns intended to guide enterprises in scaling lean and agile practices. [1] [2] Along with disciplined agile delivery (DAD) and S@S (Scrum@Scale), SAFe is one of a growing number of frameworks that seek to address the problems encountered when scaling beyond a single team.
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.
DataOps is a set of practices, processes and technologies that combines an integrated and process-oriented perspective on data with automation and methods from agile software engineering to improve quality, speed, and collaboration and promote a culture of continuous improvement in the area of data analytics. [1]
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 ...