Customers who monitor real-time data can now easily detect events or observations that do not conform to an expected pattern thanks to machine learning-based anomaly detection in Azure Stream Analytics, announced for private preview today.
Up to now, Industrial IoT customers, and others, who monitor streaming data relied on expensive custom machine learning models. Implementers needed to have intimate familiarity with the use case and the problem domain, and integrating these models with the stream processing mechanisms that required complex data pipeline engineering. The high barrier to entry precluded adoption of anomaly detection in streaming pipelines despite the associated value for many Industrial IoT sites.
Read about it in the Azure blog.