Today we share with the world the first of hopefully many milestones in TrueFoundry’s Journey. We are happy to announce that we have raised our Seed round of US$2.3m, led by Sequoia India and Southeast Asia’s Surge and joined by Eniac Ventures and other prominent Angels such as AngelList Co-founder Naval Ravikant, Deutsche Bank Global CIO Dilip Khandelwal, Head of GitHub India Maneesh Sharma, Greenhouse Software CTO Mike Boufford and Kaggle Founder Anthony Goldbloom amongst others.
Every Company will be a Data Company
While we are immensely proud of the journey we have undertaken so far to reach this place, it is still the first big step towards a 1000 mile Journey that the three of us, along with our Rockstar team, will have to undertake before we can say we have truly succeeded. Data becoming the new oil has become an old cliche, and despite that we haven't seen everyone unlocking its full potential. Machine learning 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% of ML models do not end up in production. For the models that make it to deployment, 50% fail due to absence of monitoring systems and 30% 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.
The World Needs a Simpler Solution
During our time at Facebook, we recognised that smaller companies in-market required a significantly longer time to build and deploy machine learning models as compared to big tech companies. Hence, 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. Data is the new oil and we want to enable companies to use machine learning faster and generate greater business value. We aim 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. TrueFoundry is on a mission to democratize Machine Learning production and monitoring to deliver positive value to everyone.
The biggest challenge in achieving this result is the heterogenous requirements of different organizations depending on the level of maturity of their ML development workflows - and that’s where most of the existing products fail. For example, if a startup is trying to put their first model to production, the only thing that matters to them is how do you quickly deploy your model even without worrying about best practices. On the other hand, a scale-up who has multiple teams building Machine Learning models cares about the best practices around tracking, versioning, scaling, CI/CD and monitoring. Most products fail at striking this balance where it is either too complicated to set up by a ML Developer or incurs too much technical debt for a relatively mature organization.
From Amazon Web Services (AWS), Google Cloud and Tensorflow to Kubernetes, we are platform agnostic and easily integrate with your existing stack for seamless implementation. Our platform enables ML teams to be 10x faster as a result. We make it really easy to get started (<5 mins set up time) and put models to production, but as their needs mature, the platform already supports version control, monitoring set-up, CI/CD, Auto-scaling etc. which can be quickly turned on. We think of our solution as- start like Heroku & scale like AWS.
The Rockstar Team
Finally all of these things might sound good on paper, to build and execute them is a different pain altogether. That's where we have been super fortunate in being able to assemble some of the best available talent in the Domain who share the same vision as ours. They have ensured that we stay true to our vision and build out the best of everything we can
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