Deep Checks

Deepchecks offers a platform for continuous testing, evaluation, and monitoring of machine learning (ML) models and large language model (LLM) applications. It ensures AI system quality and performance across their lifecycle. The platform empowers organizations to validate ML models and data, detecting silent failures and critical issues, maintaining trust and efficiency in AI deployments. Its capabilities are essential for managing the evolving complexities of modern AI systems.

Deepchecks was co-founded in 2019 by Philip Tannor and Shir Chorev, machine learning researchers. Their insight: organizations, despite significant investments, lacked reliable methods to detect silent ML system failures. This critical gap spurred their creation of Deepchecks as an essential solution for continuous ML system validation. Their foundational experience in leading advanced research groups provided the deep technical understanding required for this undertaking.

Deepchecks' platform empowers data science and MLOps teams, enabling control over machine learning deployments. The company envisions fostering confident AI adoption by providing comprehensive solutions for continuous model and data validation at scale. Deepchecks strives to solve present and future AI operationalization challenges, ensuring responsible and reliable AI management worldwide, thereby enhancing the overall integrity of AI-driven processes.