Rasgo Intelligence is a feature store that transforms how data scientists design and collaborate on Machine Learning (ML) features. Today, they announced a $20M Series A investment round led by Insight Partners. At the same time, Unusual Ventures continues to support Rasgo Intelligence, helping the company bring its total funding to over $25 million since its inception.
Rasgo was created by Jared Parker and Patrick Dougherty in 2020 to increase the impact of data science. It allows end users to search, clean, join, and transform data into highly curated ML features at 10x speed.
Rasgo accomplished the following in less than 12 months:
- Global enterprise clients in finance, manufacturing, alternative energy, biotech, and retail.
- Created PyRasgo, a free feature engineering experience that gives back to the data science community. It has been downloaded over 70,000 times already.
- Built a top-notch engineering and go-to-market team spread throughout three offices.
Sean Otto, Head of Data Science and Analytics at AES, has been satisfied with the company. First, here are a few about AES to provide some context:
“At AES, our mission is to accelerate the future of energy together, improving lives by delivering greener and smarter energy solutions helping everyone on a global scale take part in the evolution of energy. To accomplish this, we are leveraging Machine Learning and Artificial Intelligence across our energy businesses, but it is not a simple process.”
Regarding the collaboration with Rango, he added:
“To simplify, accelerate, and scale our AI and ML efforts, we are leveraging Rasgo to create and serve ML features as fast as we can think of them, reducing the time to designing unique and viable solutions from weeks to minutes.”
Rasgo has also been leveraged in the finance industry. Its introduction helped accelerate predictions of financial instrument prices and market volatility. Christian Peressin, Manager at Chisholm Financial Labs, a leading algorithmic hedge fund, said:
“Without Rasgo, we would have much difficulty tracking the predictive power of our features to quickly disseminate which trading strategies are delivering alpha.”
The praise doesn’t end there:
“With Rasgo, we can quickly serve and track features in our ML pipeline, which increases the velocity of our experiment and development loop and enables us to deploy new trading strategies faster.”
Jared Parker, Rasgo founder and CEO, has achieved this success by correctly evaluating issues that trouble other companies. This is what he has to say about it:
“In working with thousands of data scientists, we saw first-hand that data science projects continuously fail due to technical limitations and inefficiencies in the feature engineering process.”
He further points out the potential threats that stopped numerous projects in their tracks:
“Many ML projects never made it to production, and those that did were plagued with end user frustration due to the sheer time it took to clean, join, and transform data. At Rasgo, we are building a platform that enables data scientists to access and transform data into highly curated ML features in minutes, not weeks. Our early customers have eliminated the feature engineering bottleneck and are now creating more accurate features and models, which have allowed them to finally achieve tangible financial value from ML.”
Rasgo is also committed to accelerating the adoption of the Data Cloud for data science. In this pursuit, it has started working with Snowflake.
Snowflake Director of Technology Alliances, Tarik Dwiek, explains the company’s mission as follows:
“At Snowflake, we are continuing to build new capabilities for data scientists and data engineers.”
According to him, Rasgo remains the critical component that allows the organisation to move the needle:
“Rasgo can help our customers use Snowflake to unlock net new use cases and develop high quality ML features that are ready for production. This can represent significant acceleration of ML projects.”
Rasgo plans to use the funding round to accelerate product design, increase its engineering team, and strengthen its go-to-market function.
Patrick Dougherty, Rasgo founder and CTO, said:
“For most organisations, data science teams have been largely operating as researchers. Now, they’re being asked to operationalise their efforts and deliver quantifiable results to the bottom line.”
He acknowledges the importance of Rasgo in helping these data scientists achieve better results:
“This is a significant transition for data science teams. More often than not, unexpected process and technology limitations are unearthed, preventing teams from successfully making this transition. Rasgo’s feature store has already changed that paradigm for our customers, but there are so many more opportunities for us to amplify end user value. We’re thrilled to have the capital to hire and grow our world-class engineering team and develop new capabilities to contribute to the data science community.”
Another quote comes from George Mathew, Managing Director at Insight Partners. He states:
“Rasgo stands out as a best-in-class experience for feature engineering, helping data scientists and ML practitioners accelerate a traditionally manual process and transform raw data into actionable insights.”
He follows this statement with even more praise for the organisation’s rapid growth:
“The traction they have seen since founding exemplifies the need for a product that can help data teams increase efficiency for the whole organisation. MLOps is a burgeoning sector that we are very excited about and are now thrilled to add Rasgo to our growing portfolio.”
