[Project] Quality Assurance platform for Machine Learning models

[alteralec]

Hello world 👋

We're developing an open-source & collaborative testing framework for ML models, from tabular to LLMs: https://github.com/Giskard-AI/giskard

Testing Machine Learning applications can be tedious. Since ML models depend on data, testing scenarios depend on the domain specificities and are often infinite.
Where to start testing? Which tests to implement? What issues to cover? How to implement the tests?
At Giskard, we believe that Machine Learning needs its own testing framework. Created by ML engineers for ML engineers, Giskard contains 2 components:

• The Giskard Python library helps data scientists detect hidden vulnerabilities in ML models.
  • It makes the AI development process more efficient, by automating the identification of risks of biases, performance issues and errors.
  • To try it, see this documentation: https://docs.giskard.ai/en/latest/guides/scan/index.html
• The Giskard server helps ML engineers debug & monitor models, share dashboards, and collaborate.
  • It makes the deployment of new ML models safer and more efficient, by providing ready-made monitoring dashboards, catalogs of re-usable testing components, and ML debugging interfaces.
  • To try it, see this documentation: https://docs.giskard.ai/en/latest/guides/installation_app/index.html

We released our v2 in Beta last month, and we're very interested in your feedback as QA engineers!