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As published on wandb.ai, August 2026. Captured by usedby on Oct 3, 2026.

What happened

RadAI's machine learning team tracks and deploys its production models through Weights & Biases. They use the model registry to trigger CI/CD pipelines, so a researcher can tag an artifact and start an end-to-end deployment.

Summary written by usedby from the source page, in English. The figures are those of Weights & Biases and RadAI, not ours.

By using W&B's model registry to trigger CI/CD pipelines, researchers can tag an artifact and kick off an end-to-end deployment, cutting down on meetings and cross-team overhead.

From the page. wandb.ai, captured Oct 3, 2026

The pipeline we built, which has W&B registry as its first step, allowed us to reduce the need for synchronization across researchers and software engineers on the ML organization—less meetings, less documents.

Hasan Ali Demirci, Staff Software Engineer, Machine Learning, RadAI. Source, captured Oct 3, 2026

What the story claims, and what we checked

We compared the story with its live page on Oct 3, 2026.

  • The passage quoted aboveCheckedCopied word for word from the page, near the name of RadAI.
  • The publication dateCheckedRead from the page’s own metadata, never guessed.
  • RadAI uses Weights & BiasesCheckedConfirmed line. Latest check across sources: Oct 3, 2026.
  • The result itselfNot checkedWe quote it; we did not measure it.

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