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IBM/data-science-best-practices

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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.

active 2023-08-29 → 2026-09-15 (UTC)

Complete coverage27,979 / 27,979 hourly files (100%) · 2 absent upstream2023-08-15 → 2026-10-23 (UTC)
Events
34
Pushes
1
Pull requests
2
Issues
2
Stars
23
Forks
3

Activity over time

Daily event counts in the loaded window

Line chart, 1114 days from 2023-08-29 to 2026-09-15. Pushes: 1 total, peak 1 in a day. Pull requests: 2 total, peak 1 in a day. Issues: 2 total, peak 1 in a day. Comments: 1 total, peak 1 in a day. Stars: 23 total, peak 2 in a day.

  • Pushes
  • Pull requests
  • Issues
  • Comments
  • Stars

Stars, PRs, issues and forks are under-captured in the later part of this window. GH Archive progressively stopped capturing non-push events during 2026 — −95% or worse by the end of the window. Every series here except Pushes fades for that reason, so a decline above reflects the archive, not this repository. Pushes stay reliable throughout, so read them, and the contributor counts derived from them, as the real signal. Data health has the measurements.

Top contributors

Pushes, PRs, issues, reviews and comments — stars and forks excluded, so this is contribution rather than popularity

ContributorContributionsPushesPRsComments
sumansuhag3011
ibm-mend-app[bot]1100
renovate[bot]1010
security-ops-bot[bot]1000

Recent activity

Latest issues, pull requests and releases

Totals cover only the window loaded into ClickHouse and count events, not GitHub's lifetime totals — 23 stars here means stars gained during the window, not the repo's star count.