FeatureByte Unveils Its Self-Service Feature Platform

Print Friendly, PDF & Email

FeatureByte, an AI startup formed by a team of data science experts, announced the release of the FeatureByte self-service feature platform, which radically simplifies and automates the entire feature lifecycle to help enterprises truly scale AI across their organizations.

One of the key challenges in enterprise AI is the significant time and effort required to prepare, deploy and manage data (also called Feature Engineering). With the FeatureByte platform, enterprises can accelerate AI innovation by enabling data scientists and ML engineers to focus on creative problem solving and iterating rapidly on live data. FeatureByte automatically ensures data consistency in production, yielding better AI data and more performant models. Moreover, the self-service feature platform reduces the need for compute and personnel resources by up to 5X, lowering costs while improving data science productivity.

“One of the biggest problems in enterprise AI right now is the immense number of silos across data, modeling, operations, management, and testing,” said Hyoun Park, CEO and principal analyst at Amalgam Insights. “Feature management is an important strategic initiative for every company expecting to use AI in the long run.”

Enterprises can realize a wide range of benefits from the FeatureByte platform, including:

  • Speed and efficiency: Experiment with and deploy more features in production – faster – using only 1/5th of the compute resources and personnel.
  • Autonomy and self-service: Create features with just a few lines of code, backfill and experiment immediately, and serve features instantly, instead of waiting for weeks or months.
  • Model performance: Great features drive better model performance. Transform creative ideas into training data in minutes, while ensuring data consistency in production.
  • AI at scale: Collaborate seamlessly via FeatureByte’s intuitive graphical user interface (GUI) and self-organizing catalog. Manage feature sprawl, pipeline health and costs centrally.
  • Governance: Maintain control to provide safe and responsible AI, delivered through enterprise-grade role-based access, safety guardrails, and data pipeline approval workflows.

“The open secret of AI is that great AI starts with great data, and the most valuable enterprise data is tabular,” said Razi Raziuddin, CEO and co-founder of FeatureByte. “But the process of preparing, deploying and managing AI data is broken, with too many hands involved and lots of moving parts to orchestrate. FeatureByte is making self-service a reality for data scientists and ML engineers by extending the modern data stack and streamlining the process end-to-end. That’s a big deal for any enterprise seeking to scale and accelerate their AI efforts.”

The FeatureByte Platform complements the May 2023 release of FeatureByte SDK, an open-source software development kit that allows data scientists to use Python to create state-of-the-art features and deploy production-ready feature pipelines in minutes – all with just a few lines of code.

“Feature engineering is a critical driver of value in enterprise AI, but it’s a complex and expensive process,” said Bernardo Caldas, director of data at Mollie. “FeatureByte is taking a novel approach to simplifying the entire feature lifecycle, while putting powerful capabilities in the hands of data scientists and engineers. I believe this will spur the next wave of enterprise AI innovation and place FeatureByte as a must-have tool in the data scientist’s arsenal.”

Sign up for the free insideBIGDATA newsletter.

Join us on Twitter: https://twitter.com/InsideBigData1

Join us on LinkedIn: https://www.linkedin.com/company/insidebigdata/

Join us on Facebook: https://www.facebook.com/insideBIGDATANOW

Speak Your Mind