Deepchecks is a QA platform that inspects the production data and models so that data science leaders have control over the quality of their machine learning systems. Founded by Philip Tannor & Shir Chorev are both graduates of the Talpiot Excellence Program, and both led top tier machine learning research groups. They founded the company after realizing they had a shared passion for building guardrails for ML systems. Startup Features: ----------------------------- ML Validation of training data and ML model Observability of ML in production Alerting about various issues in live ML systems Detecting mismatches between research and production environments Quick querying of problematic production data
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