Abstract: Organizations are applying hundreds to thousands of automated workflows and machine learning models to automatically determine whether to block, accept or watch a specific transaction or an event. Even a slight change in one of these workflows may impact decision rates or introduce anomalies to your Ml models.
These anomalies in decision rates can be caused by internal changes in models and system components. It can also be caused by changes on the customers’ side: integration or decision behavior. Sometimes a change in decision rates is desirable – such as when there’s a fraud attack, entering into a new market, or a seasonal event, but sometimes it doesn’t. Therefore, it is essential to immediately identify and triage changes in decision rates to ensure users get accurate results.
Imply provides a highly scalable platform that enables real-time monitoring of ML models by allowing users to identify anomalies and generate automated alerts in real-time accurately. This live talk will demonstrate how Imply can transform streaming data from Kafka to a real-time ML model monitoring application.
Bio: Vijay has about 15+ years of experience in the data world. Vijay is currently a Senior Sales Engineer with Imply (Imply offers commercial enterprise support for open source druid). In this role, Vijay is focused on helping customers in APAC use the Imply platform (based on Apache druid). Before Imply, Vijay was with cloudera for two years helping cloudera partners position and use the cloudera platform. Before Cloudera, Vijay spent 10 years with Informatica where he was part of the team that put together connectivity for informatica cloud.