TrueFoundry, a United States-based machine learning (ML) developer platform, announced Tuesday its $2.3 million seed funding led by Sequoia India and Southeast Asia’s Surge.

Other participating investors include Eniac Ventures and prominent angels like AngelList Co-Founder Naval Ravikant, TrueFoundry said in a statement.

Other angel investors include Deutsche Bank Global CIO Dilip Khandelwal, Head of GitHub India Maneesh Sharma, Greenhouse Software CTO Mike Boufford and Kaggle Founder Anthony Goldbloom.

According to the statement, the new funding will be used to expand its specialised technology team and further product development.

“TrueFoundry was born out of the idea that no business – big or small – should miss out on the opportunities of machine learning,

“With our automated platform, data scientists and engineers are able to deploy machine learning models at the speed and maturity of big tech, cutting their production timelines from several weeks to a few hours,” said Nikunj Bajaj, Co-Founder and Chief Executive Officer of TrueFoundry.

“Data is the new oil and we want to enable companies to use machine learning faster and generate greater business value,

“Our investors and team share the belief that TrueFoundry is paving the way for innovation that will propel businesses for the future ahead, and their participation in our pre-launch funding is a great testament to that,” he added.

Headquartered in the USA, TrueFoundry was founded in June 2021 by Abhishek Choudhary, Anuraag Gutgutia and Nikunj Bajaj.

Abhishek and Nikunj are experienced software and machine learning (ML) engineers who have witnessed, built and scaled large engineering systems to drive product excellence at Facebook.

During their time at Facebook, the Co-Founders recognised that smaller companies in-market required a significantly longer time to build and deploy machine learning models as compared to 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

According to the statement, ML offers immense opportunities for businesses, 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 percent of ML models do not end up in production.

For the models that make it to deployment, 50 percent fail due to absence of monitoring systems and 30 percent have to be reverted due to scaling and latency issues that are 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 automate repetitive tasks in the ML pipeline such as infrastructure and deployments so data scientists and ML engineers can focus on higher-value, more creative tasks.

This enables businesses to continuously upgrade existing models and release new ones to gain a competitive edge.

From Amazon Web Services (AWS), Google Cloud and Tensorflow to Kubernetes, TrueFoundry is platform agnostic and easily integrates with existing stack for seamless implementation.

ML developers need less than five minutes to put models into production as hosted endpoints along with auto-scaling and monitoring dashboards are automatically available from the get-go. TrueFoundry’s platform enables ML teams to be 10 times faster as a result.

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