Finding abnormalities in time series data is critical for many systems, yet a tricky task. In this course, Microsoft Azure Cognitive Services: Anomaly Detector, you’ll learn to easily analyse your data for abnormalities using the Anomaly Detector service. First, you’ll explore the meaning of time series data and the role of machine learning to find abnormalities. Next, you’ll discover how to provision and configure Azure Cognitive Services Anomaly Detector. Finally, you’ll learn how to use the service with real data samples in the Azure Notebooks and a .NET core application. When you’re finished with this course, you’ll have the skills and knowledge of Azure Cognitive Services Anomaly Detector needed to easily detect time series data abnormalities and act on them before it is too late.
Reza, is a Microsoft Azure and Amazon AWS architect, developer and trainer. He continues helping his clients with cloud-native solutions while sharing his expertise with other developers through training and mentoring.
Course Overview Hi, everyone. My name is Reza Salehi, and welcome to my course, Microsoft Azure Cognitive Services: Anomaly Detector. I am a cloud consultant and trainer. Did you know that Microsoft Azure uses the anomaly detector API to improve security of many services such as Azure SQL Databases? In this course, we are going to work with the Azure Cognitive Service's Anomaly Detector Service. Some of the major topics that we will cover include understanding time‑series data and data anomalies, provisioning anomaly detector in the Azure portal, preparing data for the Anomaly Detector, and finally calling the Anomaly Detector API. By the end of this course, you'll know how to use Azure Cognitive Services Anomaly Detector API to find anomalies in your time‑series data. Before beginning the course, you should be familiar with the Azure Portal and the RESTful APIs. I hope you'll join me on this journey to learn Azure Anomaly Detector with the Microsoft Azure Cognitive Services: Anomaly Detector course, at Pluralsight.