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Preview: Amazon Lookout for Metrics, an Anomaly Detection Service for Monitoring the Health of Your Business

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We are excited to announce Amazon Lookout for Metrics, a new service that uses machine learning (ML) to detect anomalies in your metrics, helping you proactively monitor the health of your business, diagnose issues, and find opportunities quickly – with no ML experience required.

Lookout for Metrics uses the same technology used by Amazon to detect anomalous changes in data that are otherwise hard to find, while reducing the number of false detections. It also groups similar findings together, ranks them by severity, and provides information to determine the root cause of the anomalies.

It can be used across a wide variety of metrics such as revenue, web page views, daily active users, churn rate, transaction volume, mobile app installations, and more. Lookout for Metrics is now available in preview today.

Why Use Amazon Lookout for Metrics for Anomaly Detection?
Organizations across all industries are looking to improve efficiency in their business through technology and automation. While challenges may vary, what’s common is that being able to identify defects and opportunities early and often can lead to material cost savings, higher margins, and better customer experience. Traditionally, organizations rely on manual audits of large amounts of data, which is not scalable and is prone to human error. Others use rule-based methods based on arbitrary ranges, which are often static, do not easily adapt to seasonality changes, and lead to too many false detections.

When anomalies are detected, developers, analysts, and business owners can spend weeks trying to identify the root cause of the change. These are situations where ML can be an effective and transformational tool. However, ML algorithms need to be carefully selected, trained, tested, and deployed for each type of data – requiring a skilled team of ML experts.

Amazon has a long history of being a data-driven company, with a growing number of businesses that need to stay on top of the health of their business, operations, and customer experience. A key part of this effort over the years has involved building and improving ML technology to detect anomalies in key performance indicators (KPI) such as website visits from different traffic channels, number of products added to the shopping cart, number of orders placed, revenue for every product category, and more.

Amazon Lookout for Metrics puts the same ML technology used by Amazon in the hands of every developer. It finds anomalies in your data, groups them intelligently, helps you visualize aggregated results, and automates alerts.

Because it’s a fully managed service, it takes care of the whole ML process so you can get started quickly and focus on your core business. And most importantly, the service improves model performance continually by incorporating your real-time feedback on the accuracy and relevance of the anomalies and root cause analysis.

How Amazon Lookout for Metrics Works
You can get started with Lookout for Metrics with just a few clicks in the AWS Management Console. Without having to write any code, you connect your data to the service through the built-in data source integrations; next Lookout for Metrics trains a custom model for your data; and finally, it begins detecting anomalies for you to review and start taking action on.

Lookout for Metrics continuously monitors data stored in Amazon Simple Storage Service (Amazon S3), Amazon Relational Database Service (RDS), Amazon Redshift, Amazon CloudWatch, or SaaS integrations supported by Amazon AppFlow such as Salesforce, Marketo, Google Analytics, Slack, Zendesk, and many more.

During this phase, you can flag each field in your dataset as a measure (or KPI), dimension, or timestamp. For example, if you want to monitor abnormal changes in page views for every device type separately, then you would select page_views as the measure and device_type as the dimension.

Once your data source is configured and connected, Lookout for Metrics inspects and prepares the data for analysis and selects the right algorithm to build the most accurate anomaly detection model. This detector runs on your data at a configurable cadence (every few minutes, hourly, daily, and so on) and provides a threshold dial that allows you to adjust its sensitivity.

When detecting an anomaly, Lookout for Metrics helps you focus on what matters the most by assigning a severity score to aid prioritization. To help you find the root cause, it intelligently groups anomalies that may be related to the same incident and summarizes the different sources of impact (as shown below).

Moreover, you can configure an automatic action such as sending a notification via Amazon Simple Notification Service (Amazon SNS), Datadog, PagerDuty, Webhooks, or Slack. Or you can trigger a Lambda function, for example to temporarily hide a product on your e-commerce site when a potential pricing error is detected.

Domain knowledge and expertise can often play an important role in determining if a sudden change in a metric is expected or is an anomaly. Lookout for Metrics allows you to provide real-time feedback on the relevance of the detected anomalies, enabling a powerful human-in-the-loop mechanism. This information is fed back to the anomaly detection model to improve its accuracy.

Who’s Using Amazon Lookout for Metrics Today?
Digitata Networks offers intelligent customer, network and site-centric solutions that assist Mobile Network Operators to monitor, audit, control and automate different aspects of their network. Company CTO Nico Kruger has been pleased with the results he’s seen so far from using Lookout for Metrics.

“We discovered the improved accuracy and insights that Lookout for Metrics can bring to our existing solution and we are thrilled to use the service…we can quickly identify opportunities in addition to finding issues,” he said.

Playrix, one of the leading mobile game developers in the world, known for high-quality games such as Township, Fishdom, and Gardenscapes, is another customer that’s been working with the new service. “We experimented with our user acquisition data to understand how the service works and it quickly identified and grouped anomalies enabling us to work faster and better,” said Mikhail Artyugin, Playrix technical director.

“Lookout for Metrics has saved our team many hours of manual investigation and now notifications are viewed as actionable rather than noise, allowing our teams to easily focus on strategic priorities with less technical overhead,” he added.

“Working with almost a billion impressions every day to capture insights and intent for our customers, we need quick feedback on real data anomalies,” said Brian Ecker, a senior staff engineer at NextRoll, a marketing and data technology company with the mission to provide innovative solutions to companies to keep them growing.

“After working with the Lookout for Metrics team, we saw the improved accuracy that the new service can bring to our existing anomaly detection process and we are thrilled to start using it.”

It’s also worth noting that APN partners such as TensorIoT, Quantiphi, and Provectus have expertise in Lookout for Metrics and can help customers leverage its functionalities.

Available in Preview
Amazon Lookout for Metrics is now available in preview in US East (N. Virginia), US East (Ohio), US West (Oregon), Asia Pacific (Tokyo), and Europe (Ireland).

You can interact with the service using the AWS Management Console, the AWS SDKs and the CLI . Find out more on the technical documentation and get started quickly by joining the preview at the following link.

Request preview access to Amazon Lookout for Metrics here.


Alex Casalboni

Alex Casalboni

Alex is a Developer Advocate at AWS, he's deeply passionate about web technologies and music. He has been building web products and helping other builders learn from his experience since 2011. His coding love spreads across the Python and the JavaScript communities, and he loves contributing to open-source projects such as AWS Lambda Power Tuning.