Pecan AI, an Israel-based predictive analytics software provider for business teams and business intelligence analysts, unveiled the addition of one-click model deployment and integration with marketing automation, CRMs, and other core business systems. Pecan enables its customers now take instant actions with the help of accurate predictions provided by the software for prospects, demand, and consumer-conversion metrics generated by Pecan.
Pecan also expanded its automated predictive analytics tool for non-data scientists to include live model monitoring. The platform now constantly checks live models for indications of degeneration caused by things like internal shifts in customer behavior or external shifts in data integrity. Through this procedure, predictions are kept at a high degree of accuracy and produce a better uplift than the prior set of rules. In addition to becoming more useful over time, models can now be deployed more quickly and easily without the assistance of data engineers, allowing BI and marketing analysts to continuously monitor them for external changes in the data (like drift and leakage) and give analysts the ability to change course as necessary.
Noam Brezis, Co-Founder and CTO of Pecan AI, stated, “Predictions generated with Pecan have a direct and ongoing impact on revenue-generating activities with companies representing the full gamut of products and services. Data science models typically take many months and quarters to build, train and test – and that’s not counting the additional months it takes multiple data engineers and data scientists to connect, clean and prep the data for AI. Even when the original model works seemingly great with test data, many deployments simply fail. By automating the process and ensuring models are easy to deploy and monitor for value delivery against business goals, Pecan’s latest platform enhancements are helping customers not just build and successfully deploy more production-grade models, but are also ensuring they yield better quality predictions, saving those organizations time and money.”
