We know how hard it is to build and deploy vision AI systems that work well in practice. Most data science teams familiar with modern machine learning struggle to write production-quality software. At the same time, most software teams don’t know how to train and deploy advanced machine learning. Sure, companies like Google and Facebook (with budgets measured in the billions) run amazing algorithms at scale. But what about a Nebraska startup on a shoestring budget?
That’s why we started Sparrow Computing: to build vision AI systems for entrepreneurs so they can solve stubborn, real-world problems. We focus specifically on vision AI because cameras are everywhere and modern machine learning is an effective way to automate tedious, expensive workflows.
Our process is straightforward. We start by understanding your problem and outlining a solution. Then, we build a small prototype to validate the feasibility of a solution. Next, using a larger library of images and videos, we build a more accurate and flexible vision AI algorithm. Finally, we integrate the algorithm into your app, API, or hardware so it can help you solve real-world problems!
Ben taught himself to code in grad school during an intro stats course. Pretty quickly, he realized that he liked stats and programming more than social theory. So after a master’s degree in Sociology at the University of Nebraska, he switched to a Computational Science master’s program at Harvard. After an intense year of applied math and computer science, he became the first data scientist at Hudl.
At Hudl, he started the data engineering team and helped build the first data warehouse. He also helped start the first R&D team – a marriage between the computer vision and data science teams. Over time the R&D team became more and more applied. The group focused on becoming world class at building vision AI systems at massive scale – systems that would work well on real-world data.
In 2021, Ben took the lessons learned from a decade of building statistical and AI software and started Sparrow Computing.
Glen was first exposed to computational science and writing code while working on a masters’ degree in meteorology. At the time, his research focused on using non-traditional data sources and sensors (e.g. the barometer inside your smartphone) to improve weather prediction models, and he got to see firsthand how the thoughtful application of technological systems can make a big impact in people's lives.
After grad school, he served as an Air Force weather officer in a variety of operational forecasting roles where he gained experience leading teams and communicating complex technical information to senior leaders. By the time his military service ended, he was the director of data science and analytics for Air Force Weather’s only software factory. In that role, he led a team of meteorologists, data scientists, and software engineers through the development of mission-tailored AI applications.