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AWS Announces General Availability of Amazon Lookout for Metrics

AWS announced the general availability of Amazon Lookout for Metrics, a new fully managed service that detects anomalies in metrics and helps determine their root cause. Amazon Lookout for Metrics helps customers monitor the most important metrics for their business like revenue, web page views, active users, transaction volume, and mobile app installations with greater speed and accuracy. The service also makes it easier to diagnose the root cause of anomalies like unexpected dips in revenue, high rates of abandoned shopping carts, spikes in payment transaction failures, increases in new user sign-ups, and many more—all with no machine learning experience required. With Amazon Lookout for Metrics, there is no up-front commitment or minimum fee, and customers pay only for the number of metrics analyzed per month. To get started with Amazon Lookout for Metrics, visit https://aws.amazon.com/lookout-for-metrics/

Organizations of all sizes and across industries gather and analyze metrics or key performance indicators (KPIs) to help their businesses run effectively and efficiently. Traditionally, business intelligence (BI) tools are used to manage this data across disparate sources (e.g. structured data stored in a data warehouse, customer relationship management data residing on a third party platform, or operational metrics kept in local data stores) and create dashboards that can be used to generate reports and alerts if anomalies are detected. But effectively identifying these anomalies is challenging. Traditional rule-based methods are manual and look for data that falls outside of numerical ranges that have been arbitrarily defined (e.g. provide an alert if transactions per hour fall below a certain number), which results in false alarms if the range is too narrow, or missed anomalies if the range is too broad. These ranges are also static, and don’t change based on evolving conditions like the time of the day, day of the week, seasons, or business cycles. When anomalies get detected, developers, analysts, and business owners can spend weeks trying to identify the root cause of the change before they can take action. Machine learning offers a compelling solution to the challenges posed by rule-based methods because of its ability to recognize patterns in vast amounts of information, quickly identify anomalies, and dynamically adapt to business cycles and seasonal patterns. However, developing a machine learning model from scratch requires a team of data scientists that can build, train, deploy, monitor, and fine tune a machine learning model over time. Furthermore, a single algorithm rarely serves all of the needs of a business, which causes businesses to expend meaningfully more time and expense creating and maintaining multiple algorithms to solve different use cases. Ultimately, few organizations possess the experienced data scientists and necessary resources to successfully move past rule-based methods and realize the full potential of machine learning for detecting anomalies in their metrics.

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