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
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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.
Real-time monitor and web admin for Celery distributed task queue
A network event stream processing system, in Clojure.
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.
pmacct is a small set of multi-purpose passive network monitoring tools [NetFlow IPFIX sFlow libpcap BGP BMP RPKI IGP Streaming Telemetry].
Enterprise Network Flow Collector (IPFIX, sFlow, Netflow)
Daggy - Data Aggregation Utility and C/C++ developer library for data streams catching
Type-safe, persistent, embedded task queues and background job runner w/ SQLite. Web monitoring UI included.
Scalable Full History API Solution for Antelope (former EOSIO) based blockchains
A detective for your data. Zero-config data quality monitoring — works with dbt, Postgres, BigQuery, Snowflake. No YAML.