Ads
related to: what are devops principles examples of business management
Search results
Results From The WOW.Com Content Network
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 DSDM Agile Project Framework is an iterative and incremental approach that embraces principles of Agile development, including continuous user/customer involvement. DSDM fixes cost, quality and time at the outset and uses the MoSCoW prioritisation of scope into musts , shoulds , coulds and will not haves to adjust the project deliverable to ...
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]
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
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.
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 ...