Alpine Data has announced the general availability of the Alpine Custom Operator Framework, a methodology for developing custom algorithms that can be plugged into Alpine’s parallel machine learning engine. Complementing Alpine Touchpoints, the Custom Operator Framework enables data science and business analyst teams to create, manage and distribute frequently-requested analytic assets to business users directly into their existing activities and workflows.
According to Alpine, data science teams often receive requests to perform the same function against different datasets over and over. While these functions create a meaningful difference for business users, they are complex and multi-faceted, and data science teams are forced to deal with them tactically. The Custom Operator Framework enables a data scientist to perform a function once, and operationalize it so that it can be discovered and re-used by other teams, as well as leveraged by business users to perform future analyses themselves.
Helping organizations free up valuable data science resources and place the use of predictive models in the hands of business users, the Custom Operator Framework provides a visual development environment to operationalize proprietary methods and open source algorithms to enhance and expedite common business functions. In addition, the flexibility of the solution means data science teams and business analysts can add both proprietary and open source algorithms, models and code to the Alpine platform, and make them available as visual elements in analytics workflows.
“Alpine’s engineering team has already used the Custom Operator framework to share dozens of powerful algorithms with our customers, including those from open-source frameworks like MLlib and MADlib,” said Steve Hillion, chief product officer of Alpine Data. “Now that we’ve opened it up, everyone is able to use the same mechanism to massively increase the productivity of their data science teams.”
The Custom Operator Framework is available in Alpine 5.7 as a premium feature.