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Emeli Dralleft a comment
Hi everyone! I am Emeli, one of the co-founders of Evidently AI. I'm thrilled to share what we've been working on lately with our open-source Python library. I want to highlight a specific new feature of this launch: LLM judge templates. LLM as a judge is a popular evaluation method where you use an external LLM to review and score the outputs of LLMs. However, one thing we learned is that no...
Evidently AIOpen-source evaluations and observability for LLM apps
Evidently is an open-source framework to evaluate, test and monitor AI-powered apps.
š 100+ built-in checks, from classification to RAG.
š¦ Both offline evals and live monitoring.
š Easily add custom metrics and LLM judges.
š 100+ built-in checks, from classification to RAG.
š¦ Both offline evals and live monitoring.
š Easily add custom metrics and LLM judges.
Evidently AIOpen-source evaluations and observability for LLM apps
Evidently helps evaluate and monitor machine learning models in production. The tool generates visual reports on model performance to detect and debug issues. Follow the releases on Github: https://github.com/evidentlyai/evidently
Evidently AIOpen-source monitoring for machine learning models
Emeli Dralleft a comment
Hi there! I am Emeli, one of the makers of Evidently. I worked on over 50 applied machine learning projects, and I know many ways they can go wrong in production. Data drifts, data breaks, real-world patterns change, or model performance drops on a specific segment. We are building a tool I wish I had when working on real-world ML applications. Right now, the tool generates interactive reports...
Evidently AIOpen-source monitoring for machine learning models