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aws-samples/regulated-mlops-using-amazon-sagemaker

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This project shows steps to build an end to end MLOps architecture that covers data prep, model training, realtime and batch inference, build model registry, track lineage of artifacts and model drift detection. It utilizes SageMaker Pipelines that offers machine learning (ML) to orchestrate SageMaker jobs and author reproducible ML pipelines.

active 2023-08-282025-12-23 (UTC)

Complete coverage26,707 / 26,707 hourly files (100%) · 2 absent upstream2023-08-152026-08-31 (UTC)
Events
7
Pushes
0
Pull requests
1
Issues
0
Stars
4
Forks
1

Activity over time

Daily event counts in the loaded window

Line chart, 849 days from 2023-08-28 to 2025-12-23. Pushes: 0 total, peak 0 in a day. Pull requests: 1 total, peak 1 in a day. Issues: 0 total, peak 0 in a day. Comments: 0 total, peak 0 in a day. Stars: 4 total, peak 1 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
dependabot[bot]1010

Recent activity

Latest issues, pull requests and releases

  • Pull request#1dependabot[bot]2023-11-02 21:28

Totals cover only the window loaded into ClickHouse and count events, not GitHub's lifetime totals — 4 stars here means stars gained during the window, not the repo's star count.