Read the pitch deck Rasgo used to raise $20 million to help data scientists build machine learning features 10x faster
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- Founded only a year ago, MLops startup Rasgo is built on cloud data warehouse Snowflake.
- It aims to help data scientists turn raw data into machine learning features 10x faster than usual.
- It just raised a $20 million Series A and is aiming to partner with BigQuery and Databricks next.
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Tech workers Jared Parker and Patrick Dougherty first met three years ago when they “took data scientists out for lunch and dinners and just heard them complain about their current world,” Parker told Insider.
The pain point they heard again and again, according to Parker, was “Why am I spending all my time extracting, exploring, cleaning, joining, transforming raw data into a set of features that can be consumed by my model?”
Their answer to that frustration is Rasgo Intelligence, a startup they founded a year ago during the height of the pandemic, that helps data scientists prep their data, reuse code, and ultimately build machine learning models much more efficiently.
On Thursday, the New York-based startup raised a $20 million Series A led by Insight Partners with participation from Unusual Ventures. This latest round brings its total funding to over $25 million; the startup declined to disclose its valuation.
Rasgo offers a “feature store,” or a single place that holds the raw data, data layers, and services that data scientists and engineers use to run and share code among machine learning models.
“We think of the feature store as having three dimensions: The first is enabling users to transform raw data into features at 10x velocity,” Parker said. “Secondarily is sharing features across data scientists and models, and the third is continuously serving features to models in production to produce financial value.”
George Mathew, a managing director at Insight Partners who led the round, told Insider that what made Rasgo stand out from other startups in the feature store category, such as Tecton, is its integration with cloud data warehouse giant Snowflake.
“One of the things that we just kept noticing was that a lot of the existing capability in that market, you were kind of setting up all these separate systems and separate capabilities,” Mathew said. “Having it built natively on top of a cloud native data warehouse is a much more scalable approach for most enterprises.”
Indeed, machine learning operations or MLOps is a rapidly expanding market that coincides with the popularity of cloud data warehouses like Snowflake, Databricks, as well as Google’s BigQuery and Amazon’s RedShift.
It’s a market that Deloitte predicts will reach nearly $4 billion by 2025. That, in turn, has enabled startups like Rasgo to piggyback on the trend and offer specialized services for data scientists and engineers.
“It’s literally this tailwind we’re seeing in the market with how much cloud native data warehouses have really taken off in the last half decade in particular,” Mathew said.
Parker says Rasgo isn’t stopping at Snowflake: it plans to work with BigQuery, Databricks, and other major cloud data warehouses next.
With this latest infusion of capital, the startup plans to hire for its product and engineering teams, with the goal of growing the team from 10 to 35 engineers in the next 18 months.
Parker said Rasgo will also expand its community marketing and education to further engage with the data science community and grow its open source offering, PyRasgo, which lets users keep track of their data experiments.
“I would say our biggest opportunity is community adoption and getting this open source product out into the community to be adopted and drive value,” Parker said. “Our mission as a company is to enable every data scientist to generate valuable and trusted insights from data in under five minutes.”
Read the pitch deck that Rasgo exclusively shared with Insider to raise its $20 million Series A round: