Rasgo 20m Series
At the speed of thought, Rasgo 20m Series allows data scientists to discover, clean, connect, convert, and feature raw data into analysis-ready data. Rasgo 20m Series provides efficiency gains throughout the data extraction and exploration, data preparation, and feature engineering phases of the data scientist’s lifecycle, allowing them to expedite the crucial but monotonous pre-model preparation activities.
Rasgo 20m Series Introduction
When Jared Parker and Patrick Dougherty “took data scientists out for lunch and dinners and basically listened to them gripe about their existing reality,” Parker told Insider, they first met three years ago. Parker claims that the most common complaint they received was a variation on the theme, “Why am I spending all my effort extracting, exploring, cleaning, joining, and converting raw data into a collection of features that can be ingested by my model?” As a result of this annoyance, they launched Rasgo 20m Series Intelligence, a business, a year ago during the height of the epidemic, to aid data scientists in preparing their data, reusing code, and more quickly and effectively developing machine learning models. Rasgo 20m Series provides what it calls a “feature store,” which is a centralized location for storing the raw data, data layers, and services that data scientists and engineers need to execute and distribute code for machine learning models.
A feature store may help users “convert raw data into features at 10x velocity,” as Parker put it. “Thirdly,” as in “constantly providing features to models in production to provide financial value,” is “sharing features between data scientists and models.”
According to Insider interviews with George Mathew, managing director at Insight Partners, who led the round, and other investors, Rasgo 20m Series connection with cloud data warehousing behemoth Snowflake is what sets it apart from other businesses in the feature store area like Tecton. Mathew pointed out that the market’s current capabilities were fragmented since “you were sort of building up all these different platforms and various capacities.” It’s considerably more scalable for most businesses to have it developed natively on top of a cloud-native data warehouse. In fact, the popularity of cloud-based data warehouses like Snowflake, Data bricks, Google Big Query, and Amazon Redshift has coincided with the fast growth of the machine learning operations business, often known as MLOps. In fact, Deloitte estimates that by 2025, the market will be worth close to $4 billion.
This has paved the way for other businesses to capitalize on the need for data scientists and engineers by creating platforms like Rasgo. Specifically in the past five years, “cloud-native data warehouses have really taken off,” thus there is a “tailwind” in the industry, as Mathew put it. Parker claims that Rasgo has ambitions to integrate with additional large cloud data warehouses in addition to Snowflake, such as Big Query and Data bricks. The company intends to use the newly acquired funds to expand its product and engineering teams; specifically, it hopes to increase its current headcount of 10 engineers to 35 over the next 18 months. In addition to growing its open source product, PyRasgo, which allows users to keep track of their data trials, Parker said that Rasgo would increase its community marketing and education efforts to more actively connect with the data science community. Jared Parker and Patrick Dougherty co-founded Rasgo in 2020 with the goal of accelerating the impact of data science by giving consumers the tools they need to rapidly explore, clean, connect, and turn data into highly curated ML features.
Rasgo 20m Series has managed to achieve the following in a little over a year
Business clients worldwide in the banking, industrial, biotech, retail, and renewable energy sectors With the goal of giving back to the data science community, we released PyRasgo, a free feature engineering experience that has had over 70,000 downloads too far. Recruited, trained, and led a world-class staff in three different offices to handle all aspects of product development and market expansion. According to AES’s Head of Data Science and Analytics Sean Otto, “leveraging Machine Learning and Artificial Intelligence across our energy businesses is not a simple process.” However, AES is committed to accelerating the future of energy together, improving lives by delivering greener and smarter energy solutions that allow everyone on a global scale to participate in the evolution of energy.
Rasgo 20m Series is being used to speed up forecasts of financial instrument pricing and market volatility in the financial sector, and to simplify, accelerate, and scale AI and ML activities more generally. Christian Press, Manager at Chisholm Financial Labs, a leading algorithmic hedge fund, said, “Without Rasgo, we would have much difficulty serving and tracking features in our ML pipeline, which increases the velocity of our experiment and development loop and enables us to deploy new trading strategies faster.” Rasgo 20m Series allows the firm to quickly serve and track features in its ML pipeline, allowing it to quickly disseminate which trading strategies are delivering alpha.
“In working with hundreds of data scientists, we learned firsthand that data science initiatives repeatedly fail due to technological restrictions and inefficiencies in the feature engineering process,” stated Jared Parker, founder and CEO of Rasgo. “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 has allowed them to achieve higher levels of success.” Rasgo 20m Series has built an interface with Snowflake as part of its commitment to hastening the spread of the Data Cloud in the data science community. Snowflake’s Director of Technology Alliances, Tarik Dwiek, stated, “At Snowflake, we are continuing to create new capabilities for data scientists and data engineers.” With Rasgo’s assistance, our clients can utilize Snowflake to discover brand-new use cases and create high-quality, production-ready machine learning features. Rasgo 20m Series plans to utilize the proceeds from this round of investment to expedite product development, recruit top-tier technical talent, and strengthen its go-to-market efforts.
According to Patrick Dougherty, founder, and chief technology officer of Rasgo 20m Series, “for most enterprises, data science teams have been essentially working as researchers. Now, they’re being challenged to operationalize their efforts and produce tangible outcomes to the bottom line.” We’re excited to have the resources to hire and grow our world-class engineering team and develop new capabilities to contribute to this shift, which is a major one for data science teams.
Often, unexpected process and technology limitations are uncovered, preventing teams from successfully making this transition. Rasgo 20m Series feature store has already changed that paradigm for our customers. According to George Mathew, Managing Director of Insight Partners, “Rasgo stands out as a best-in-class experience for feature building, allowing data scientists and ML practitioners to expedite a typically tedious process and convert raw data into meaningful insights.” Since its inception, Rasgo has witnessed significant growth, and we are happy to add them to our expanding portfolio.