More than a year after announcing plans to automate the feature engineering phase of artificial intelligence projects, Seattle-based startup Kaskada Inc. is bringing its first product to market.

Kaskada says it aims to democratize feature engineering, an often laborious process that requires data scientists to select, clean and validate the data to be fed into machine learning training models prior to moving them into production.

A model intended to predict housing prices, for example, would be feature engineered with predictor data such as the square footage of properties, number of bedrooms and location. The larger and more complete the training data set, the better the results.

The resources required to collect data and move machine learning models into production can be so significant that the capabilities are out of reach of all but the largest companies. Kaskada says its platform features a collaborative interface for team engineering and a proprietary data infrastructure for computing across event-based data and serving features in production.

“We are focused on building the bridge between training and production,” said Davor Bonaci, Kaskada’s chief executive and a former software engineer at Google LLC and Microsoft Corp. “We are launching a self-service platform to help data scientists get work into production by automating infrastructure. You can onboard and don’t have a big adoption curve or need to get everybody in your organization you agree to try it.”

The company’s self-service platform is a self-contained data science studio with pre-built machine learning models and the feature vectors needed to support them provided via an application program interface. “You get up-to-the-moment feature vectors for functions like real-time fraud detection,” Bonaci said. “You don’t have to write data pipelines or process streaming data. We run the data processing needed for the model.”

Event-driven focus

Kaskada’s platform has undergone some changes since it was announced, the most significant of which is a greater focus on event-driven data collection. That’s a type of processing that makes decisions in response to real-time events such as mouse clicks and transactions.

Event-driven processing is especially useful in scenarios like predicting the probability that a customer will buy a product or that a credit card transaction will be fraudulent. Real-time data handling requires an efficient data infrastructure to calculate features at arbitrary points in time and to deliver them to both training and production environments. “We have built a lot of functionality to think in terms of time,” Bonaci said.

The company has also focused more of its attention on automating the data science process rather than data engineering. Those two functions are supposed to work in tandem but frequently fail to communicate effectively because data scientists are focused on data and engineers on getting models into production.

“There can be friction getting into production because science and engineering teams have different values,” Bonaci said. “We reduce the friction needed to get work into production.”

Kaskada is a cloud-native service that customers can deploy in their own cloud instances, run as a managed service or install on local infrastructure. The company offers a distinctive pricing model that includes a free tier with limited data capacity, curated public datasets, sample projects and individual commit and version histories. Paid plans support team development, batch data uploads, direct data connection and real-time features. Details weren’t provided.

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There is a growing need for statisticians and data analysts nationwide, as computational advancements have afforded researchers the ability to generate and analyze mass quantities of data. To meet the demand, employment opportunities for statisticians are expected to grow by 33 percent within the decade.

Southern Illinois University Edwardsville’s Center for Predictive Analytics is leading a statewide fellowship program funded by the U.S. Department of Agriculture’s National Institute of Food and Agriculture through its Research and Extension Experiences for Undergraduates program.

Under the direction of C-PAN Director Carolyn Butts-Wilmsmeyer, the project, Preparing Undergraduates for New Frontiers in Data Analysis: Experiential Learning in Applied Statistics (ELIAS) Fellows, is concurrently training undergraduate students in real-world data analysis and hands-on research in a greenhouse, laboratory or field setting.

Institutional collaborators include the University of Illinois at Urbana-Champaign, Illinois State University, Northeastern Illinois University and Parkland Community College. 

“The overall goal of the ELIAS fellowship program is to produce students who can meet the need for an increasingly data-driven workforce, particularly in the life sciences,” Butts-Wilmsmeyer explained. “Upon graduation, students in statistics and data science are placed in multidisciplinary teams consisting of chemists, biologists and business personnel.

“However, current training in data science and statistics often does not occur in laboratory, greenhouse, field or other applied research settings, making it difficult for graduates to understand the limitations in these research environments and to communicate findings across disciplinary bounds. Through this program, students are placed in a two-year, dually immersive research experience in applied statistics/data science and a laboratory, greenhouse or field research environment, based on the students’ interests.”

Fellow Sam Garcia, an environmental science major at NEIU, is in her second year of the program.

“I have been passionate about environmental science since I learned about climate change in elementary school,” she said. “I particularly became interested in data-driven research because quantifying and analyzing data brings order and significance to the information that can be found through science.”

Under the mentorship of Geddes, and in collaboration with Urban Rivers, Garcia is analyzing the effects of artificial floating wetlands on macroinvertebrate communities in the Chicago River.

“The ELIAS program has provided me the opportunity to carry out my own independent research project, which is preparing me for graduate school,” Garcia said. “After graduate school, I plan to pursue a research career in the marine or atmospheric science field at NOAA, NASA or a similar organization. My long-term intentions are to use science as a tool to incite change that will help preserve the environment.”

Butts-Wilmsmeyer notes the USDA’s recognition of the need for programs that support the recruitment and training of traditionally underrepresented groups in the food and agricultural sciences. As such, the ELIAS Fellows’ recruitment efforts emphasize women and minorities, as well as transfer students from community colleges.

“While the fellowship program is open to all students in the agricultural and life sciences, and all fellows will be provided with unfailing support, we recognize that there may be some hurdles which female students, transfer students and underrepresented minorities may face at a higher frequency than their classmates,” Butts-Wilmsmeyer explained. “Our mentor team actively works with all of our fellows to identify ways to overcome any hurdles they may face during the completion of their degree and progression toward their desired careers.”

Participating students receive full funding for their research, as well as a $7,250 stipend each year of the fellowship. They will present their findings at UIUC Agronomy Day, symposia at their respective institutions and at a scientific conference of their choosing.

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