Splice Machine, provider of a scale-out SQL database with built-in machine learning, is launching the Splice Machine Feature Store, helping more companies operationalize machine learning by reducing the complexity of feature engineering.
"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."
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.
According to the vendor, the Splice Machine Feature Store solves some of the biggest pain points of operationalizing machine learning, including:
- Reducing the effort of feature engineering
- Helping to solve for governance issues, such as bias, drift, or regulatory oversight
- Scaling data science operations
- Reducing monetary loss from the creation of inaccurate models
This will help data scientists realize numerous benefits, including:
- Achieving faster deployments of AI/ML into production by reusing features and avoiding duplicative feature engineering
- Spending 80% 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
For more information about this news, visit https://splicemachine.com.