IBM / data-science-best-practices

The goal of this repository is to enable data scientists and ML engineers to develop data science use cases and making it ready for production use. This means focusing on the versioning, scalability, monitoring and engineering of the solution.

Date Created 2021-05-03 (3 years ago)

Commits 32 (last one about a year ago)

Stargazers 86 (0 this week)

Watchers 9 (0 this week)

Forks 29

License cc-by-4.0

Ranking

RepositoryStats indexes 579,555 repositories, of these IBM/data-science-best-practices is ranked #319,190 (45th percentile) for total stargazers, and #223,408 for total watchers. Github reports the primary language for this repository as Ruby, for repositories using this language it is ranked #5,218/7,632.

IBM/data-science-best-practices is also tagged with popular topics, for these it's ranked: python (#13,730/21870), machine-learning (#5,053/7877), ai (#2,224/3806), data-science (#1,421/2096), devops (#909/1314), analytics (#500/713), mlops (#265/410), best-practices (#241/309), datascience (#110/163)

Other Information

IBM/data-science-best-practices has 1 open pull request on Github, 5 pull requests have been merged over the lifetime of the repository.

Homepage URL: https://ibm.github.io/data-science-best-practices/

All Topics

ai ibm mlops devops python analytics datascience data-science bestpractices best-practices machine-learning

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Watcher History

Github watchers over time, collection started in '23

Recent Commit History

10 commits on the default branch (main) since jan '22

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Issue History

No issues have been posted

Languages

The primary language is Ruby but there's also others...

updated: 2024-10-25 @ 04:31pm, id: 364060335 / R_kgDOFbMerw