“Umbrella has dramatically decreased the number of support tickets and increased customer satisfaction.”

Learn how leading telcos are using Umbrella's ML-based anomaly detection to ensure business support systems can keep pace with the high level of service required for mission-critical applications.

Learn how leading telcos are using Umbrella and its real-time alerts to automatically monitor their network operations for proactive incident management.

Learn how telcos are using Umbrella to automatically monitor their OSS, BSS and CEM layers and use real-time alerts for proactive incident management.

Learn how leading adtech companies -- including Rubicon Project, Uprise and NetSeer -- are leveraging the power of machine learning to find outliers in time series data and turn them into valuable business insights.

Leveraging the vast amount of business data available today to better meet customer needs and detect business incidents presents organizations with the challenge of whether to build their own anomaly detection system or buy one ready-made. Before organizations make this critical decision, it is important to weigh the benefits and challenges of each approach.

Part 3 of our Ultimate Guide to Anomaly Detection explains the process of identifying, ranking and correlating abnormal behavior in time series. Read on to find out.

Part 2 of our Ultimate Guide to Anomaly Detection presents a general framework for learning normal behavior for time series. Can a seasonal pattern be assumed?What is the importance of modeling seasonality? Why does real-time detection at scale requires online adaptive learning algorithms? Read on to find out.

The first in our 3-part guide to anomaly detection covers the components necessary to designing a machine learning-based anomaly detection system.