# How RadAI uses Weights & Biases

As published on [wandb.ai](https://wandb.ai/site/customers/radai/), August 2026. Captured by usedby on 2026-10-03.

- Company: [RadAI](https://www.usedby.ai/companies/radai.md)
- Tool: [Weights & Biases](https://www.usedby.ai/tools/weights-and-biases.md)
- Teams: Machine Learning

## What the story says

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.

> 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

## What usedby checked

We compared the story with its live page on 2026-10-03.

- Checked: the passage quoted above is copied word for word from the page, near the name of RadAI.
- Checked: the publication date is read from the page’s own metadata, never guessed.
- Checked: RadAI uses Weights & Biases. Confirmed line. Latest check across sources: 2026-10-03.
- Not checked: the result itself. We quote it; we did not measure it.

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Source: https://www.usedby.ai/case-studies/radai-weights-and-biases · How we check: https://www.usedby.ai/methodology