Our great sponsors
-
-
WorkOS
The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.
But I may have it haha. What we propose in the blog post instead of relying solely on data drift is using performance estimation methods (eg: https://github.com/NannyML) with them you can estimate the performance of the ml model without having access to ground truth.
NOTE:
The number of mentions on this list indicates mentions on common posts plus user suggested alternatives.
Hence, a higher number means a more popular project.
Related posts
- Introduction to NannyML: Model Evaluation without labels
- Detecting silent model failure. NannyML estimates performance for regression and classification models using tabular data. It alerts you when and why it changed. It is the only open-source library capable of fully capturing the impact of data drift on performance.
- [HIRING][Full Time, Part Time, Temporary, Internship, Freelance] Data Science Intern (Remote)
- What do you think about Detecting Silent ML Failure with an Open Source Python library?
- Can I estimate the impact of data drift on performance?