Evidently is an open-source ML and LLM observability framework. Evaluate, test, and monitor any AI-powered system or data pipeline. From tabular data to Gen AI. 100+ metrics.
- data-drift
- jupyter-notebook
- pandas-dataframe
- machine-learning
Repository Index
47,133 repositories indexed
1–5 of 5 · Most stars
Deepchecks: Tests for Continuous Validation of ML Models & Data. Deepchecks is a holistic open-source solution for all of your AI & ML validation needs, enabling to thoroughly test your data and models from research to production.
- machine-learning
- ml
- model-validation
- data-validation
Data validation toolkit for assessing and monitoring data quality.
- data-validation
- tabular-data
- data-testing
- data-quality
Canonical GTFS Validator project for schedule (static) files.
- gtfs-dataset
- gtfs-validator
- gtfs-zip
- gtfs-feed
Data Quality and Observability platform for the whole data lifecycle, from profiling new data sources to full automation with Data Observability. Configure data quality checks from the UI or in YAML files, let DQOps run the data quality checks daily to detect data quality issues.
- data-ops
- data-quality
- data-quality-checks
- data-quality-measurement