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Amazon Web Services Releases the Amazon Lookout for Metrics

By CIOReview | Tuesday, April 6, 2021

Amazon Web Services announced the availability of Amazon Lookout for Metrics, the new fully managed service that recognizes metrics anomalies.

FREMONT, CA : Amazon Web Services, Inc. (AWS), an Amazon.com, Inc. company, recently announced the availability of Amazon Lookout for Metrics, the latest fully managed service that identifies metrics anomalies and assists in understanding the root cause. Customers can use Amazon Lookout for Analytics to monitor the most relevant business metrics, such as sales, web page visits, active customers, purchase volume, and mobile app installs, more quickly and accurately. Without any help from machine learning, the service makes it easier to determine the root cause of anomalies such as unexplained drops in sales, high rates of abandoned shopping carts, rises in payment transaction failures, increases in new customer sign-ups, and many more. There is no minimum cost or up-front commitment with Amazon Lookout for Metrics, and customers pay for the number of metrics analyzed each month.

To help their companies operate effectively and efficiently, organizations of all sizes and across industries collect and evaluate metrics or key performance indicators (KPIs). Business intelligence (BI) tools are typically used to monitor this data through disparate channels (for example, organized data stored in a data center, customer relationship management data stored on a third-party database, or operational metrics stored in local data stores) and built dashboards that can be used to produce reports and notifications if anomalies are identified.

Detecting these anomalies is difficult. Traditional rule-based approaches are manual and search for data that falls outside of arbitrarily specified numerical ranges, resulting in false alarms or missed anomalies if the range is too small or too wide. These ranges are also constant and do not change with evolving circumstances such as the time of day, day of the week, seasons, or business cycles. When anomalies are discovered, developers, researchers, and business owners may spend weeks attempting to pinpoint the problem's source before taking action.

Machine learning is a promising solution to the challenges faced by rule-based approaches due to its capability to identify patterns in numerous information, quickly recognize anomalies, and dynamically respond to market cycles and seasonal patterns. Developing a machine learning model from scratch needs a team of data scientists capable of building, training, deploying, monitoring, and fine-tuning a machine learning model over time. A single algorithm seldom meets every requirement of a company, due to which they have to spend more time and money designing several algorithms to address various use cases.

"From marketing and sales to telecom and gaming, customers in all industries have KPIs that they need to be able to monitor for potential spikes, dips, and other anomalies outside of normal bounds across their business functions. But catching and diagnosing anomalies in metrics can be challenging, and by the time a root cause has been determined, much more damage has been done than if it had been identified earlier," said Swami Sivasubramanian, Vice President of Amazon Machine Learning for AWS. "We're excited to deliver Amazon Lookout for Metrics to help customers monitor the metrics that are important to their business using an easy-to-use machine learning service that takes advantage of Amazon's own experience in detecting anomalies at scale and with great accuracy and speed."