AWS Machine Learning Blog
Category: Amazon Lookout for Metrics
Transitioning off Amazon Lookout for Metrics
In this post, we provide an overview of the alternate AWS services that offer anomaly detection capabilities for customers to consider transitioning their workloads to.
Build a loyalty points anomaly detector using Amazon Lookout for Metrics
Today, gaining customer loyalty cannot be a one-off thing. A brand needs a focused and integrated plan to retain its best customers—put simply, it needs a customer loyalty program. Earn and burn programs are one of the main paradigms. A typical earn and burn program rewards customers after a certain number of visits or spend. […]
Visualize your Amazon Lookout for Metrics anomaly results with Amazon QuickSight
One of the challenges encountered by teams using Amazon Lookout for Metrics is quickly and efficiently connecting it to data visualization. The anomalies are presented individually on the Lookout for Metrics console, each with their own graph, making it difficult to view the set as a whole. An automated, integrated solution is needed for deeper […]
Build an air quality anomaly detector using Amazon Lookout for Metrics
Today, air pollution is a familiar environmental issue that creates severe respiratory and heart conditions, which pose serious health threats. Acid rain, depletion of the ozone layer, and global warming are also adverse consequences of air pollution. There is a need for intelligent monitoring and automation in order to prevent severe health issues and in […]
Easily customize your notifications while using Amazon Lookout for Metrics
We are excited to announce that you can now add filters to alerts and also edit existing alerts while using Amazon Lookout for Metrics. With this launch, you can add filters to your alerts configuration to only get notifications for anomalies that matter the most to you. You can also modify existing alerts as per […]
Identify potential root cause in business-critical anomalies using Amazon Lookout for Metrics
We are excited to launch a causal contribution analysis capability in Amazon Lookout for Metrics that helps you to understand the potential root causes for the business-critical anomalies in the data. Previously, you were only given the root causes for a single anomaly per measure. You had to analyze to determine if causal relationships existed […]
Seamlessly connect Amazon Athena with Amazon Lookout for Metrics to detect anomalies
Amazon Lookout for Metrics is an AWS service that uses machine learning (ML) to automatically monitor the metrics that are most important to businesses with greater speed and accuracy. The service also makes it easier to diagnose the root cause of anomalies, such as unexpected dips in revenue, high rates of abandoned shopping carts, spikes […]
Detect anomalies using Amazon Lookout for Metrics and review inference through Amazon A2I
Proactively detecting unusual or unexpected variances in your business metrics and reducing false alarms can help you stay on top of sudden changes and improve your business performance. Accurately identifying the root cause of deviation from normal business metrics and taking immediate steps to remediate an anomaly can not only boost user engagement but also […]
Detect anomalies in operational metrics using Dynatrace and Amazon Lookout for Metrics
Organizations of all sizes and across all industries gather and analyze metrics or key performance indicators (KPIs) to help their businesses run effectively and efficiently. Operational metrics are used to evaluate performance, compare results, and track relevant data to improve business outcomes. For example, you can use operational metrics to determine application performance (the average […]
Simplify and automate anomaly detection in streaming data with Amazon Lookout for Metrics
Do you want to monitor your business metrics and detect anomalies in your existing streaming data pipelines? Amazon Lookout for Metrics is a service that uses machine learning (ML) to detect anomalies in your time series data. The service goes beyond simple anomaly detection. It allows developers to set up autonomous monitoring for important metrics […]