TrueFoundry, a machine learning (ML) developer platform, has announced the completion of a US$2.3 million seed funding led by Sequoia India and Southeast Asia’s Surge.
Eniac Ventures and prominent angels like AngelList Co-Founder Naval Ravikant also participated.
Other participants include Deutsche Bank Global CIO Dilip Khandelwal, Head of GitHub India Maneesh Sharma, Greenhouse Software CTO Mike Boufford, and Kaggle Founder Anthony Goldbloom.
TrueFoundry will use the funds to expand its specialised technology team and further product development.
Headquartered in the US, TrueFoundry was founded in June 2021 by Abhishek Choudhary, Anuraag Gutgutia and Nikunj Bajaj.
During their stint at Facebook, the Co-Founders recognised that smaller companies in the market required a significantly longer time to build and deploy machine learning models than big tech companies. This led to the founding of TrueFoundry, which automates repetitive tasks in the ML pipeline to accelerate ML deployment and live endpoint monitoring.
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Machine learning offers immense business opportunities, yet the development and launch of ML models is a time-intensive and complex process for software engineers, ML engineers and data scientists.
As a result, almost 90 per cent of ML models do not end up in production. For the models that make it to deployment, 50 per cent fail due to the absence of monitoring systems, and 30 per cent have to be reverted due to scaling and latency issues often overlooked during the data training stage.
While large companies can bridge this gap by deploying large, high-end ML platform teams to design and launch ML models, it is less feasible for smaller companies and startups to commit such high investments while building their companies.
TrueFoundry aims to transform the process by automating parts in the ML pipeline that can be automated and empowering ML developers to test and launch models in production quickly and with as much autonomy as possible.
“TrueFoundry was born out of the idea that no business – big or small – should miss out on machine learning opportunities. With our automated platform, data scientists and engineers can deploy machine learning models at the speed and maturity of big tech, cutting their production timelines from several weeks to a few hours. Data is the new oil, and we want to enable companies to use machine learning faster and generate greater business value,” said CEO Nikunj Bajaj.
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