Anomaly Detection


An Introduction to Anomaly Detection

In early 1900, Sakashi Toyoda invented a loom that automatically stops when the thread breaks, limiting the need for someone to watch the machine constantly. This approach was later named “Jidoka” and became one of the two pillars of the TPS (Toyota Production System) with just-in-time production representing the second pillar.


Detect security threats with anomaly detection rules

Securing your environment requires being able to quickly detect abnormal activity that could represent a threat. But today’s modern cloud infrastructure is large, complex, and can generate vast volumes of logs. This makes it difficult to determine what activity is normal and harder to identify anomalous behavior. Now, in addition to threshold and new term –based Threat Detection Rules , Datadog Security Monitoring provides the ability to create anomaly


Preventing Shopping Cart Abandonment with Anomaly Detection

The global pandemic has changed B2C markets in many ways. In the U.S. market alone in 2020, consumers spent more than $860 billion with online retailers, driving up sales by 44% over the previous year.eCommerce sales are likely to remain high long after the pandemic subsides, as people have grown accustomed to the convenience of ordering online and having their goods – even groceries – delivered to their door.


What's new with BigQuery ML: Unsupervised anomaly detection for time series and non-time series data

When it comes to anomaly detection, one of the key challenges that many organizations face is that it can be difficult to know how to define what an anomaly is. How do you define and anticipate unusual network intrusions, manufacturing defects, or insurance fraud? If you have labeled data with known anomalies, then you can choose from a variety of supervised machine learning model types that are already supported in BigQuery ML.


Science of Network Anomalies

Today’s networks have evolved a long way since their early days and have become rather complicated systems that comprise numerous different network devices, protocols, and applications. Consequently, it is practically impossible to have a complete overview of what is happening in the network or whether everything in the network works as it should. Eventually, network problems will arise.

Rethinking Anomaly Detection

John Sipple, Staff Software Engineer in AI, at Google Cloud presents Google's story about rethinking anomaly detection. In 2019, Google Smart Buildings asked the team to develop an AI-based fault-detection solution to help find and fix problems in climate control devices in large office buildings. Technicians were dissatisfied with conventional outlier approaches because they didn’t give the necessary insight to predict, diagnose and intervene. The result was a distributed deep-learning solution that provides explanations to aid understanding, prioritizing and fixing faults. We applied it to other domains, like data center monitoring and fraud detection, and then open-sourced the MADI machine learning algorithm behind it. We’ll describe our vision of how AI will shape the future of interpretable anomaly detection.

Bridge the gap in your OSS by adding an AI brain on top

Telecom companies monitor their network using a variety of monitoring tools. There are separate fault management and performance management platforms for different areas of the network (core, RAN, etc.), and infrastructure is monitored separately. Although these solutions monitor network functions and logic – something that would seem to make sense — in practice this strategy fails to produce accurate and effective monitoring or reduce time to detection of service experience issues.