{"id":1678,"date":"2019-02-13T00:00:00","date_gmt":"2019-02-13T08:00:00","guid":{"rendered":""},"modified":"2025-06-17T01:22:15","modified_gmt":"2025-06-17T08:22:15","slug":"anomaly-detection-using-built-in-machine-learning-models-in-azure-stream-analytics","status":"publish","type":"post","link":"https:\/\/azure.microsoft.com\/en-us\/blog\/anomaly-detection-using-built-in-machine-learning-models-in-azure-stream-analytics\/","title":{"rendered":"Anomaly detection using built-in machine learning models in Azure Stream Analytics"},"content":{"rendered":"\n

Built-in machine learning (ML) models for anomaly detection in Azure Stream Analytics significantly reduces the complexity and costs associated with building and training machine learning models. This feature is now available for public preview worldwide both in the cloud and on IoT Edge.<\/p>\n\n\n\n

What is Azure Stream Analytics?<\/h2>\n\n\n\n

Azure Stream Analytics is a fully managed serverless PaaS offering on Azure that enables customers to analyze and process fast moving streams of data, and deliver real-time insights for mission critical scenarios. Developers can use a simple SQL language (extensible to include custom code) to author and deploy powerful analytics processing logic that can scale-up and scale-out to deliver insights with milli-second latencies.<\/p>\n\n\n\n

Traditional way to incorporate anomaly detection capabilities in stream processing<\/h2>\n\n\n\n

Many customers use Azure Stream Analytics to continuously monitor massive amounts of fast-moving streams of data in order to detect issues that do not conform to expected patterns and prevent catastrophic losses. This in essence is anomaly detection.<\/p>\n\n\n\n

For anomaly detection, customers traditionally relied on either sub-optimal methods of hard coding control limits in their queries, or used custom machine learning models. Development of custom learning models not only requires time, but also high levels of data science expertise along with nuanced data pipeline engineering skills. Such high barriers to entry precluded adoption of anomaly detection in streaming pipelines despite the associated value for many Industrial IoT sites.<\/p>\n\n\n\n

Built-in machine learning functions for anomaly detection in Stream Analytics<\/h2>\n\n\n\n

With built-in machine learning based anomaly detection capabilities, Azure Stream Analytics reduces complexity of building and training custom machine learning models to simple function calls. Two new unsupervised machine learning functions are being introduced to detect two of the most commonly occurring anomalies namely temporary and persistent.<\/p>\n\n\n\n