Rasgo Raises $25M to Further Feature Engineering

Rasgo, a data engineering startup that aims to simplify feature engineering prep work in the cloud, has raised a $20 million Series A, bringing its total funding to $25 million. The company also added another arrow to its feature engineering quiver in the form of PyRasgo, which provides a Python interface for its feature store.

Rasgo was founded in 2020 by Jared Parker and Patrick Dougherty to help data scientists and other users more quickly explore, clean, join, and transform data into features that can be plugged into machine learning models.

Up to this point, its primary offerings have included RasgoSQL, a library that auto-generates data transformations in SQL from the comfort of a Jupyter data science notebook, giving users a “pandas-like” experience, the company says, as well as Rasgo UI, which provides a full client for working with the library.

On March 29, the company unveiled its latest creation, PyRasgo, which is designed to help data scientists profile their data features within dataframes, track their feature engineering process, and visualize which features are most important through histograms and statistical tables.

Once a user is happy with the data in their dataframe, PyRasgo will automatically calculate importance scores for each feature in the dataframe. The company explains: “PyRasgo calculates this feature importance by building a CatBoost model, calculating the SHAP values and using the mean absolute value of the SHAP values as the feature importance.”

The software was developed over the past year based on insight gleaned from its community, the company says.

“Over the past 12 months, we have been writing code and engaging with the data science community each and every day,” the company writes in its blog. “We have been in the weeds with over 400 end users, constantly diving into their biggest pain points and obsessing over delivering solutions that save them time and heartache.”

PyRasgo shows users a histogram of their dataframe (Image courtesy Rasgo)

Rasgo’s Series A, which was announced a week ago, was led by Insight Partners with participation from Unusual Ventures. The company says it plans to use the funding to accelerate product development, hire more engineers, and build out its go-to-market function.

There are a lot of ways that Rasgo can help automate the transition that data science teams are undergoing as they move from being researchers to developing production products, says Dougherty, Rasgo’s CTO.

“This is a significant transition for data science teams,” Dougherty says in a blog. “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 enduser 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.”

Related Items:

Feature Stores Emerging as Must-Have Tech for Machine Learning

2021: The Year of the Feature Store

What is Feature Engineering and Why Does It Need To Be Automated?