Has Anyone Used the product "Weights & Biases" for AI QA Testing?

Discussions
,
1

Hi all!

I’m considering spending time on the product Weights & Biases (W&B) - developed by CoreWeave - into my QA process for my new AI startup.

While W&B is widely used for ML model tracking and evaluation, I’m curious about its effectiveness from a QA perspective I.e. tracking and refining ‘Evals’.

I want to refine and objectively measure the accuracy of our algorithm and the appropriateness of its feedback for non-deterministic evaluations of art, guided by analysis of the object+ genres + tropes etc

  • Has anyone here used W&B for test case management, monitoring model drift, or automating evaluation workflows? I’ve never heard of it but it must be quite big if Microsoft are endorsing it and giving it away.

  • How does it compare to other QA-focused tools for debugging AI applications?

  • Any lessons learned or pitfalls to watch out for?

Microsoft is currently offering free access to W&B through its Microsoft for Startups Founders Hub, so I’m exploring whether it’s worth integrating before committing long-term.

Looking forward to hearing your experiences / recommendations!

3 Likes
2

Yes, at QAonCloud, we’ve explored Weights & Biases (W&B) primarily for ML experiment tracking, but it’s also useful in AI QA testing workflows.

While W&B isn’t a traditional QA tool, it helps us in:

Tracking model performance across datasets
Comparing runs and identifying anomalies
Visualizing changes in model behavior after code or data updates

For AI-driven applications, especially those using computer vision or NLP, W&B gives valuable visibility that complements functional and regression testing. It doesn’t replace test automation tools but works well alongside them in AI product pipelines.

3

Thank you very much Sivesh

I think a way to qualitatively and objectively measure the accuracy and expected results of your algorithm’s output over time as it is refined and optimised is essential for a proper AI evaluation strategy.

I don’t think there is as much traffic about quality assurance of algorithms as there might be, but I’m sure this discipline will evolve as time goes by. Good to be relatively early to the party!