Examples of using Evidently to evaluate, test and monitor ML models.
Code tutorials showing specific use cases, Evidently functionality or example dataset analysis. Come with blog posts or detailed explanations.
If you are new to Evidently, explore the official Quickstarts first.
| Title | Code | Blog | Author | Tags |
|---|---|---|---|---|
| How to evaluate RAG | Jupyter notebook | Tutorial | Evidently team | llm, text_descriptors |
| How to run and debug LLM regression tests | Jupyter notebook | - | Evidently team | test_suites, llm, text_descriptors |
| How to create an LLM judge | Jupyter notebook | - | Evidently team | llm, text_descriptors |
| How to monitor LLM testing results | Jupyter notebook | Tutorial | Evidently team | test_suites, llm, text_descriptors |
| How to run regression testing for LLMs | Jupyter notebook | Tutorial | Evidently team | test_suites, llm, text_descriptors |
| How to create ML model cards | Jupyter notebook | Tutorial | Evidently team | reports, data_quality, model_quality, classification |
| How to use descriptors to monitor text data | Jupyter notebook - with model training | Tutorial | Evidently team | reports, test_suites, text_data, nlp, data_drift, text_descriptors |
| How to monitor NLP models in production | Jupyter notebook - with model training | Tutorial | Evidently team | reports, text_data, nlp, data_drift, text_descriptors, classification |
| How to break a model in 20 days. A tutorial on production model analytics. | Jupyter notebook | Tutorial | Evidently team | reports, data_drift, model_quality, regression |
| How to compare models pre-deployment. | Jupyter notebook | Tutorial | Evidently team | reports, model_quality, classification |
| How to design ML monitoring. Tutorial from CS 329S: Machine Learning Systems Design. | Jupyter notebook | Tutorial | Evidently team | reports, data_drift, model_quality, regression |