Follow Datanami:
August 1, 2022

FeatureByte Raises $5.7M to Fix the Weakest Link in AI

FeatureByte, the developer of a feature engineering platform, has launched from stealth with a $5.7 million seed round. The Boston-based startup bills its SaaS solution as specifically made for data scientists to simplify the creation, serving, managing, and monitoring of machine learning features.

A feature, also known as a variable, is any measurable input used for a predictive machine learning model. As the first step in ML model development, feature engineering is the process of applying domain knowledge (e.g., business knowledge, mathematics, and statistics) to extract analytical representations from raw data. In order to choose the most useful features for a particular model, data scientists must select, manipulate, and transform raw data into a format that can be directly consumed by ML models. There are plenty of data preparation tools made for the data analytics realm that can automate data preparation, but there is a lack of automation tools built specifically for AI model workloads.

Given this data preparation limitation, feature engineering and management is an intricate process that can be slow and expensive. According to Gartner, features are some of the most highly curated and refined data assets due to how much time, effort, and skill is involved with their creation. FeatureByte says that despite this importance, many organizations do not have an effective feature management system. Additionally, the company says that feature engineering is the weakest link in scaling AI because it “requires the confluence of three unique skills – domain knowledge, data science, and data engineering. Even in organizations with mature AI practices, these areas of expertise live in silos. And at the intersection of these silos lies a ton of friction.”

Solving the problem of these disparate silos of expertise is the goal for FeatureByte co-founders Razi Raziuddin and Xavier Conort, both of whom are DataRobot alumni. Raziuddin, FeatureByte’s CEO, scaled DataRobot from 10 to 850 employees and led its go-to-market strategy. Conort, CPO at FeatureByte, was chief data scientist at DataRobot and built its R&D data science team. The startup’s $5.7 million seed round was led by Glasswing Ventures and Tola Capital, and the company plans to use the funds to scale its R&D and go-to-market operations.

“Our team has successfully launched AI deployments for hundreds of organizations worldwide. However, the one constant challenge enterprises face is feature engineering and management. Xavier and I formed FeatureByte to radically simplify the process for data scientists and application developers,” said Raziuddin. “The market is extremely fragmented, with siloed solutions addressing only pieces of the puzzle. We are developing a solution from first principles to address full cycle featuring engineering and are excited to partner with Glasswing Ventures and Tola Capital to drive this vision and mission forward.”

FeatureByte has plans for its cloud-based platform to have integration capabilities with Snowflake and Databricks. FeatureByte will be available for early users via an invite-only beta program. To learn more, visit this link.

Related Items:

Feature Stores Emerging as Must-Have Tech for Machine Learning

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

Artificial Intelligence and Machine Learning Are Headed for A Major Bottleneck — Here’s How We Solve It