Resources / Blogs / Scribble Data raises funding to scale feature store

Scribble Data raises funding to scale feature store

We are thrilled to announce that we’ve just closed our first round of funding to help us scale and deliver our Feature Store product, Enrich, in international markets for enterprise-grade Machine Learning products.  Our investors are data-driven leaders from companies like Google and Amazon, from the US and India. 

Scribble Enrich, our feature store product, allows data scientists to develop and manage production-ready datasets (features) that are used to train ML models faster, and with confidence. It is in a fast-emerging category of ML infrastructure products called DevOps for Data.

A recent enterprise survey indicated that almost 85% of companies are putting models into production. As ML models take centre stage across companies and industries, the ability to productionize them faster will be key to deriving value out of ML in the enterprise. Feature stores like Enrich are a crucial building block of this production infrastructure. 

Venkata, Scribble Data’s CEO, had this to say about our origins – “I’ve been a data scientist myself, and in my work, I deeply felt the need for a trusted, easily accessible, always available, and relevant source of data for all my analysis and modeling. Our customer deployments have helped grow and test the product, and prepare for tomorrow.” 

Our market is fast-growing mid-sized Enterprises that are looking to deploy Machine Learning into production. The days of R&D, exploratory analysis and the like are giving way to companies looking to operationalize their ML. This is a large emerging market that will need to either build or buy a feature store if they want to be able to reliably productionize models. Enrich offers them a highly customizable option that’s flexible, scalable, and most importantly, robust. 

Vivek Gour, a board member at Affle and Indiamart, former board member of Makemytrip, and ex-CFO of Genpact is one of the investors in this round. He had this to say – “Scribble addresses a key challenge that is being discussed in boardrooms across industries – slow deployment of Machine Learning solutions. Companies today need frameworks like Scribble’s platform, Enrich, to speed work up 10x, and do so with confidence in the underlying data to retain their competitive edge in the new covid world.”

This funding round will be used to deepen our feature store offering, and to expand into the US and Europe. Our existing clients have come from sectors such as edtech, e-commerce, and retail.

Scribble Data was founded by Venkata Pingali (B.Tech in CS from IIT-B, PhD @USC) and Indrayudh Ghoshal (B.Engg in EE from McGillU and MBA @UToronto), and operates from Bangalore and Toronto.

Contact: 

Dominique Hoover

Business Operations

dominique@scribbledata.io

[1] O’reilly. AI adoption in the enterprise 2020 https://www.oreilly.com/radar/ai-adoption-in-the-enterprise-2020/

Related Blogs

February 22, 2024

Exploring OpenAI’s SORA and Text-to-Video Models: A Complete Guide

In every epoch, some moments redefine the course of human history. The discovery of fire illuminated the dark. The invention of the wheel set humanity in motion. The creation of the printing press unfurled the banners of knowledge across the globe. Unironically, we may be standing at the threshold of another such transformative moment with […]

Read More
February 15, 2024

Building AI Assistants: A Comprehensive Guide

For years, a giant mystery confounded the world of medicine. How do proteins fold?  The answer, elusive, held the key to life itself. Then, a heroic AI agent – AlphaFold, emerged from DeepMind’s depths. It tackled the giant. And won. AlphaFold produces highly accurate protein structures The implications? Beyond staggering. AlphaFold is just the beginning. […]

Read More
February 8, 2024

How GenAI and Machine Learning are Transforming Actuarial Science

In the late 17th century, Edmond Halley sat by candlelight. He pored over numbers. Charts. Life tables. Halley, an astronomer by trade, ventured into uncharted waters. He sought to understand mortality, to predict life spans. His work laid the foundation for modern actuarial science. It was a time of discovery, of manual calculations, and limited […]

Read More