Ask any data scientist and they’ll say that data prep is a long, arduous process.
On average, these highly skilled professionals spend nearly 38% of their time cleaning and loading data rather than strategic operations. Razi Raziuddin and Xavier Conort, seasoned AI professionals, experienced this bottleneck firsthand. The process of preparing raw data and transferring it to machine learning models was time consuming, expensive, and labor intensive.
These challenges lead them to launch FeatureByte, a self-service platform that empowers data scientists and machine learning engineers to swiftly transform raw data into production-ready pipelines. Users can input their use cases and the system recommends optimal feature engineering methods, ranks them by relevance, and provides clear explanations. The result? An expedited AI feature creation, experimentation, and management process.
“We’re taking a very complex process that could take weeks or months to accomplish and reducing it to minutes and hours.“
— Razi Raziuddin
FeatureByte improves latency, privacy, and scalability while solving for common pain points such as duplicate names, feature complexity, and data governance.
“Addressing these data challenges can help organizations of all sizes to harness AI’s full potential,” said Raziuddin. “Imagine the AI advancements possible if pipelines could be deployed in minutes instead of months?”
Here’s how FeatureByte works. Imagine a finance company aiming to use AI to forecast potential credit card fraud. Such a task requires a wide range of features — which are individual, measurable pieces of data — to be added to an AI model. In this example, features may include disparate data such as geographic data, purchase histories, and more. Creating an AI model for this could take months and require incredible coordination from data science and AI engineering teams. With Featurebyte, the timeline for creating production-ready pipelines shrinks dramatically.
A boost from LIFT Labs: FeatureByte’s Accelerated Growth Path
FeatureByte was selected for the Comcast NBCUniversal LIFT Labs Accelerator: Enterprise AI from a pool of hundreds of applicants hoping to join the six-week program. The accelerator is built to help startups connect with Comcast to explore potential pilot programs and partnerships. FeatureByte saw this as an invaluable chance to gain insights on how enterprises manage data analytics and AI.
“The more I learned about the accelerator, the more I became excited about participating, and getting the opportunity to learn from different business units within the company,” said Raziuddin. “We consider ourselves fortunate to have the opportunity to explore scaling AI.” Being selected for the LIFT Labs Accelerator is a significant accolade for FeatureByte, a venture that’s still in its early stages. Raziuddin and Conort met while working at an AI company and bonded over a shared conviction that the future of AI depended on someone solving the data and feature engineering problem. Solving the problem became an obsession, leading them to launch FeatureByte to bridge the gap between raw data and running accurate machine learning models in production.
After setting up shop in Boston, FeatureByte raised a $5.7 million seed round in 2022 which was directed toward research and development, market expansion, and hiring leading data scientists. Now FeatureByte is targeting business-to-consumer companies in banking, financial services, fintech, telecom, media, gaming, and healthcare.
As the demand for AI-centric data pipelines surges, FeatureByte wants to equip data professionals with the tools to be able to prioritize innovation and experimentation.
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