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This article describes a real-time cloud-based anomaly detection solution that utilizes AI and machine learning to detect abnormal behavior in data. It covers use cases such as monitoring, root cause analysis, data quality, and intelligent alerting.
This article presents a solution for real-time anomaly detection using component metrics aggregated in time series. The solution evolves from deep learning models to gradient boosting models to neocortex neural networks, providing real-time data ingestion, training, and detection, as well as the ability to onboard new metrics and detect anomalies in a continuous time frame.
Retailers are constantly looking for ways to improve the speed of delivery in online retail. One way to achieve this is by implementing options such as Buy Online Pick Up in Store (BOPUS) and Ship from Store (SFS). However, these options bring about challenges in terms of inventory reservation. This article discusses the inventory optimization problem for BOPUS and SFS and provides a case study on how machine learning methods can be used to solve this problem.
