Today, we are announcing the preview of Humanloop for Large Language Models (LLMs). Sign up to be part of the closed beta.

Machine learning test metrics should always be calculated with credible intervals. Credible intervals give you upper and lower bounds on test performance so you know how big your test needs to be and when to trust your models. Humanloop Active Testing can give you uncertainty bounds on your test metrics and makes this easy.

We're really excited to announce Programmatic 4.0 with support for No-Code Templates — simple UI-based labeling functions that anyone can understand even if they don't know how to program.

There are huge advantages to labeling in-house such as quality control, faster iteration and privacy. New technologies like transfer learning, programmatic labeling and active learning are now making it practical for the best teams.

Humanloop Programmatic is now available for early access. A powerful weak labeling tool to rapidly annotate your NLP datasets.