Israeli MLOps startup Deepchecks raised a $14 million Seed funding round, led by Alpha Wave Ventures with participation from Hetz Ventures and Grove Ventures. In line with its funding round, the startup also announced the release of its open-source solution aimed at comprehensive validation of machine learning models and data sets, both in research and production phases.
The open-source, Python-based tool by Deepchecks allows users to customize and reuse components for exhaustive testing of machine learning models and datasets. It also includes monitoring capabilities and root cause analysis for production environments, alongside a comprehensive user interface. The solution ensures the continuity of machine learning validation from research to production, enabling ML Engineers to leverage the deep knowledge of Data Scientists in later stages of the model or data lifecycle. It also provides Code-Level Root Cause Analysis, which accelerates the root cause analysis cycles, saving up to 70% of time usually spent on initial analysis.
Founded in 2019 by Talpiot and 8200 unit alumni, Philip Tannor and Shir Chorev, Deepchecks’ novel solution facilitates a shift in the MLOps field by adding systematic validation to every step of the machine learning lifecycle.
With over 500K downloads, Deepchecks’ solution is used by industry giants like AWS, Booking.com, and Wix, as well as in regulated sectors like finance and healthcare.
Their open-source tool also integrates with a number of platforms like W&B, HuggingFace, Databricks, H2O, pytest, Airflow, zenml, and CML.
The latest funding round brings the startup’s total funding to $18.3 million.