FeatureByte, an AI startup formed by a team of data science experts, announced the release of its open-source FeatureByte SDK. The SDK allows data scientists to use Python to create state-of-the-art features and deploy feature pipelines in minutes – all with just a few lines of code. FeatureByte automatically generates complex, time-aware SQL to perform feature transformations at scale in cloud data platforms such as Databricks and Snowflake.
By enabling a self-service data environment for data scientists, organizations can realize a series of benefits from the FeatureByte SDK, 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.
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“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.”
FeatureByte helps data scientists transform creative ideas into training data swiftly, to deliver more accurate ML models using tools like Jupyter Notebooks. Organizations can streamline machine learning data pipelines in order to rapidly deploy powerful AI solutions.
“We’ve made the FeatureByte SDK available in open source, so that every data scientist and engineer can focus on creativity, experimentation and problem solving, rather than data pipelines and plumbing,” said Xavier Conort, CPO and co-founder of FeatureByte. “FeatureByte helps data scientists derive the most accuracy from their data, capturing a variety of signal types beyond the traditional Recency, Frequency, and Monetary metrics,”
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