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Splice Machine Releases Feature Store for Feature Engineering and Democratized Machine Learning

With a hybrid transaction/analytical processing (HTAP) SQL database, the Feature Store enables ML models to integrate past and present data to improve predictions.

Note: TDWI’s editors carefully choose vendor-issued press releases about new or upgraded products and services. We have edited and/or condensed this release to highlight key features but make no claims as to the accuracy of the vendor's statements.

Splice Machine, provider of a scale-out SQL database with built-in machine learning, announced the Splice Machine Feature Store. It is designed to help companies operationalize machine learning by reducing the complexity of feature engineering, and allow data scientists to make informed decisions based on real-time data.

“The capacity to create, share, explain, and reliably reproduce features for a given model is paramount to the success of a data science team,” said Monte Zweben, CEO, Splice Machine. “The old way of doing things meant data science operations were simply not scalable. The Splice Machine Feature Store enables you to harness complex analytics in real time and transform real-time data into features, so your models are never uninformed. It also stores feature history making training set creation a single click.”

Feature engineering is the most time-consuming and expensive task of the data science life cycle. As companies work to operationalize machine learning, current approaches are not scalable because data science productivity is too low to enable widespread adoption. Simplifying the data science workflow by providing necessary architecture and automating feature serving with feature stores are two of the most important ways to make machine learning easy, accurate and fast at scale.

The Splice Machine Feature Store solves some of the challenges of operationalizing machine learning, including:

  • Reducing the effort of feature engineering
  • Helping to solve for governance issues, such as bias, drift, and regulatory oversight
  • Scaling data science operations
  • Reducing monetary loss from the creation of inaccurate models

This will help data scientists realize benefits such as:

  • Achieving faster deployments of AI/ML into production by reusing features and avoiding duplicative feature engineering
  • Spending 80 percent less time on feature engineering
  • Developing more informative models via automatic aggregation of raw data
  • Gaining predictive accuracy of models with near real-time feature updates and consistent training sets

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