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This is an accepted version of this page This is the latest accepted revision, reviewed on 31 January 2025. Integration of software development and operations DevOps is the integration and automation of the software development and information technology operations [a]. DevOps encompasses necessary tasks of software development and can lead to shortening development time and improving the ...
Version Control is an important link in a DevOps toolchain and a component of software configuration management. Version Control is the management of changes to documents, computer programs, large web sites, and other collections of information. [8] A summary of Version Control related activities are: Non-linear development; Distributed development
Configuration management. Configuration management helps with identifying the source code for all features that have been completed to date and maintaining a history of changes to classes as feature teams enhance them. Regular builds. Regular builds ensure there is always an up-to-date system that can be demonstrated to the client and help ...
Agile methods are mentioned in the Guide to the Project Management Body of Knowledge (PMBOK Guide 6th Edition) under the Product Development Lifecycle definition: Within a project life cycle, there are generally one or more phases that are associated with the development of the product, service, or result.
Azure DevOps, formerly known as Team Foundation Server (TFS) and Visual Studio Team System (VSTS), is a Microsoft product that provides version control (either with Team Foundation Version Control (TFVC) or Git), reporting, requirements management, project management (for both agile software development and waterfall teams), automated builds, testing and release management capabilities.
Release management is the process of managing, planning, scheduling and controlling a software build through different stages and environments; it includes testing and deploying software releases. [ 1 ] [ 2 ]
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 ...
It typically involves dividing software development work into smaller, parallel, or sequential steps or sub-processes to improve design and/or product management. The methodology may include the pre-definition of specific deliverables and artifacts that are created and completed by a project team to develop or maintain an application. [1]