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2. DevOps - Wikipedia

   en.wikipedia.org/wiki/DevOps

   Automation is an important part of DevOps. Software programmers and architects should use "fitness functions" to keep their software in check. [2] According to Neal Ford, DevOps, particularly through continuous delivery, employs the "Bring the pain forward" principle, tackling tough tasks early, fostering automation and swift issue detection. [3]

3. DevOps Research and Assessment - Wikipedia

   en.wikipedia.org/wiki/DevOps_Research_and_Assessment

   DevOps Research and Assessment (abbreviated to DORA) is a team that is part of Google Cloud that engages in opinion polling of software engineers to conduct research for the DevOps movement. [ 1 ] The DORA team was founded by Nicole Forsgren , Jez Humble and Gene Kim .

4. DevOps toolchain - Wikipedia

   en.wikipedia.org/wiki/DevOps_toolchain

   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

5. CI/CD - Wikipedia

   en.wikipedia.org/wiki/CI/CD

   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.

6. Continuous delivery - Wikipedia

   en.wikipedia.org/wiki/Continuous_delivery

   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.

7. MLOps - Wikipedia

   en.wikipedia.org/wiki/MLOps

   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 ...