Deepchecks is the emerging standard to make machine learning safe and predictable from research to production
Today Deepchecks announced the general availability of its solution for continuous machine learning validation and $14 million in seed funding led by Alpha Wave Ventures with participation from Hetz Ventures and Grove Ventures.
Machine learning is taking the world by storm, creating a tremendous amount of value. The market is expected to grow from $26 billion in 2023 to $226 billion in 2030 at a CAGR of 36%. But only half of machine learning models make it to production, and the ones that do often end up being over time and budget or failing in spectacular ways. In other words, most machine learning models could use some improvement.
Machine learning is transitioning from long research projects to something organizations expect to regularly deploy to production in software-like development cycles. It took classical software development decades to establish processes and tools to help deliver projects on time and to quality. It took ChatGPT just two months to reach 100 million users. But machine learning has more moving parts and is more opaque, which makes it less safe and predictable in comparison.
The way to move machine learning forward is to apply testing and validation, drawing on lessons learned from software. Deepchecks helps practitioners, developers and other stakeholders go beyond MLOps and gain visibility and confidence every step of the way: from development to deployment and operation in production.
“It makes little sense to deploy and monitor software that has not been thoroughly and systematically tested first. Yet, this is what happens today with machine learning applications. Deepchecks brings a new approach to MLOps, improving models by adding validation to every step of the machine learning lifecycle” said Deepchecks co-founder and CEO Philip Tannor.
“Deepchecks introduces a community-driven MLOps framework that enables people from data scientists and developers to C-level executives to have a clear picture of how machine learning applications behave from research to production”, said Deepchecks co-founder and CTO Shir Chorev.
Tannor and Chorev co-founded Deepchecks three years ago. They were listed in the Forbes 30 under 30 list and have known and worked with each other since they were 18. From the IDF’s Talpiot program to the elite 8200 intelligence unit, they picked up machine learning and got real-world exposure, earning distinctions along the way. They started Deepchecks as a “traditional” MLOps solution, but quickly realized that the way to cater to data scientists and developers at scale, is with an offering that has open source at its core.
Deepchecks offers an open source solution that lets users reuse and tailor components to holistically test machine learning models and datasets. These components were developed leveraging years of collective expertise and have been battle-tested in production.
The solution also includes monitoring and root cause analysis for production environments as well as a comprehensive UI. Deepchecks has over 500K downloads and is used at the likes of AWS, Booking.com, and Wix, as well as in highly regulated industries such as finance and healthcare. The enterprise version includes additional collaboration and security features.
“Quality assurance is often assigned to people who are in the beginning of their careers. In machine learning, however, it’s often the most senior, highest paid person in the room who is tasked with this. That’s because as opposed to other domains QA has not yet been systematized for machine learning, so it remains a dark art. Deepchecks is here to address this, moving machine learning and businesses forward,” said Yuval Rozio, Director of Alpha Wave Ventures.
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