Summary: In our last two episodes we learned a bit about the Azure Anomaly Detector service. We first learned a bit about what it is and how it can be used. Then we looked into bringing the service on premises using containers. As with any service of this kind sometimes it takes a little tweaking to get things to the next level. In this episode Qun Ying gives three amazing tips that will help when we design our monitoring application architecture. Learn More! Check out the cool demo on this episode from https://aka.ms/adDemo.Find the detailed documentation on the best practices on https://aka.ms/adBest.Check out the overview of the API service on https://aka.ms/AnomalyDetector.Create your first Anomaly Detector resource on Azure: https://aka.ms/adNew.Join Anomaly Detector Containers preview through https://aka.ms/adContainer.Join "Anomaly Detector Advisors" public community to connect with the product team and other members in the community through https://aka.ms/adAdvisorsJoin.Fast Forward: [00:46] Tip 1: Batch mode versus streaming mode in Anomaly Detector.[02:76] Tip 2: How to detect anomalies from streaming time series data with Anomaly Detector?[05:00] An interactive demo of streaming anomaly detection.[06:36] Python code of streaming anomaly detection with Anomaly Detector APIs.[07:39] Tip 3: How to pre-process data for Anomaly Detector APIs?[08:00] What if the input time series is not evenly distributed?[09:04] How to improve the accuracy if the data has seasonal patterns?
