SAN FRANCISCO, CA--(Marketwired - Nov 18, 2015) - Alpine Data today announced the general availability of the Alpine Custom Operator Framework, a flexible methodology for developing custom algorithms that can be plugged directly 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.
Data science teams receive requests to perform the same function against different data sets time and time again. 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. For example, a customer management team at a financial institution is building credit models, and needs to fill in missing fields for individuals with incomplete profiles. A data scientist might approximate these fields with aggregates from other individuals with more complete profiles. The function to compute these aggregates might be quite complex, repetitive, and time-consuming to build. In many cases, teams in different parts of the organization will re-create the same function over and over again, introducing inconsistencies and re-work.
The Custom Operator Framework enables a data scientist to perform this function once, and operationalize that Custom Operator so that it can be discovered and re-used by other teams, and even leveraged by business users to perform future analyses themselves. The Custom Operator Framework fulfills a critical role to help organizations free up valuable data science resources and place the power of predictive models in the hands of business users.
The Custom Operator Framework provides a visual development environment to easily operationalize proprietary methods and open-source algorithms to dramatically enhance common business functions. The flexible nature of the Custom Operator Framework means data science teams and business analysts can add their 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 Steven Hillion, Chief Product Officer, 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.
To learn more about Custom Operator Framework, read the Alpine Data blog: "The Extensibility of the Alpine Platform."
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