Real-time anomaly detection has applications across industries. From network traffic management to predictive healthcare and energy monitoring, detecting anomalous patterns in real-time is helping businesses derive actionable insights in multiple sectors.
However, as data complexity increases, modern data science is simplifying and streamlining traditional approaches to anomaly detection.
How can today’s enterprises ride the modern data science wave to effectively address the evolving challenges of real-time anomaly detection? And what are the key differentiators businesses must look for, to identify a platform that meets their needs?
Let’s explore how modern data science is transforming anomaly detection as we know it.
Meetup – Stream Processing with Apache Kafka, Flink and Spark
November 2, 2019 - November 2, 2019 Hotstar office (Bangalore)
Anomaly Detection with Machine Learning at Scale (India)
Sep 27, 2019 | 11:30 am IST