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A software build is the process of converting source code files into standalone software artifact(s) that can be run on a computer, or the result of doing so. [1]In software production, builds optimize software for performance and distribution, packaging into formats such as '.exe'; '.deb'; '.apk'.
In end-user development an artifact is either an application or a complex data object that is created by an end-user without the need to know a general programming language. Artifacts describe automated behavior or control sequences, such as database requests or grammar rules, [1] or user-generated content. Artifacts vary in their maintainability.
GitHub (/ ˈ ɡ ɪ t h ʌ b /) is a proprietary developer platform that allows developers to create, store, manage, and share their code. It uses Git to provide distributed version control and GitHub itself provides access control , bug tracking , software feature requests, task management , continuous integration , and wikis for every project ...
is typically some part of the source code, the ownership concept have been also applied to other artifacts of the software development as diverse as an entire project or a single software bug; [4] the owner ("who?") might be an individual developer or a group that might include authors of the code, reviewers , and managers. [ 5 ]
Sonatype Nexus Repository is a software repository manager, available under both an open-source license and a proprietary license. [1] It can combine repositories for various programming languages, so that a single server can be used as a source for building software.
Azure DevOps Server, 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.
Whenever a new workflow with all components is shared via GitHub, anyone can try it on a different machine, with different environment and using slightly different choices (compilers, libraries, data sets). Whenever an unexpected or wrong behavior is encountered, the community explains it, fixes components and shares them back as described in. [4]
DVC pipeline is focused on the experimentation phase of the ML process. Users can run multiple copies of a DVC pipeline by cloning a Git repository with the pipeline or running ML experiments. They can also record the workflow as a pipeline, and reproduce [28] it in the future. Pipelines are represented in code as yaml [29] configuration files ...