We are excited to announce a new AI module on MS Learn: Identify abnormal time-series data with Anomaly Detector.

Introduction

Training a Machine Learning model for Anomaly Detection is challenging. There are multiple types of data patterns: Spikes/Dips, periodic wavy pattern, inclining pattern, declining pattern etc. In real life, data patterns from time series data are inconsistent and unpredictable. There is no one algorithm that fits all the patterns. As a result, developers or data scientists have to continuously train a new model to accurately detect issues. In this learn module, you will explore how Azure Anomaly Detector is able to automatically select an algorithm or model based on the data pattern it observes in real-time.

Hands-on Exercises

The module provides hands-on exercises to read time-series data from a smart meter device; send data to the cloud using IoT Hub; and use Azure Anomaly Detector service to spot issues in the real-time data. The module provides a FREE sandbox environment for all the hands-on exercise.

Benefits of Azure Anomaly Detector

The following are some of the Anomaly Detector features that will be explored in the module:

• Auto selection of the best algorithm in real-time based on the data pattern.
• Root cause detection when you have multiple contributors of the anomaly.
• Custom Training & deploying AI model
• REST API & SDK support

Let’s get started

After completing this lesson, you will be able to use the Azure Anomaly Detector to build AI solutions that are reliable and ensure that your processes run smoothly. You will understand how the Azure Anomaly Detector APIs work and how to apply the AI service to you time-series data analytics. Get started here. Happy Learning!