Home Content News FeatureByte Launces Open Source Version Of FeatureByte SDK

FeatureByte Launces Open Source Version Of FeatureByte SDK


Data scientists can build powerful features with just a few lines of code and deploy pipelines in a matter of minutes thanks to software development kits (SDKs).

Data science professionals founded the AI business FeatureByte, which today announced the availability of its open source FeatureByte SDK. With just a few lines of code, the SDK enables data scientists to utilise Python to build cutting-edge features and quickly deploy feature pipelines. In order to perform feature transformations at scale in cloud data platforms like Databricks and Snowflake, FeatureByte automatically creates complicated, time-aware SQL.

Organisations can get a number of advantages from the FeatureByte SDK by offering a self-service data environment for data scientists, including:

  • Accelerated AI innovation: Data scientists can focus on creative problem solving and iterating rapidly on live data, rather than worrying about the “plumbing.”

  • Better business decisions: The FeatureByte SDK delivers better AI data that yields higher performing models, resulting in better business decisions for an organization.

  • Higher productivity with reduced costs: The self-service data environment delivers up to 10x compute efficiency for training, and requires 1/5th of the resources to deploy feature pipelines.

Using tools like Jupyter Notebooks, FeatureByte enables data scientists to quickly convert original concepts into training data for machine learning models that are more accurate. Machine learning data pipelines can be streamlined by businesses in order to quickly implement effective AI solutions.

“At FeatureByte, our goal is to radically simplify feature engineering and management to help enterprises truly scale AI across their organizations,” said Razi Raziuddin, CEO and co-founder of FeatureByte. “FeatureByte offers a self-service environment for data scientists so they have a consistent, scalable way to prepare, serve and manage data across the entire lifecycle of a model.”



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