Solution architecture: Anomaly detection with machine learning
The services used by modern IT departments generate large volumes of telemetry data to track various aspects of operational health, system performance, usage insights, business metrics, alerting and many others. Often, however, monitoring and gathering insights from all of this data is not fully automated and can be error prone, making it hard to effectively and accurately determine the health of the system at any given point in time.
This customisable anomaly-detection solution uses machine learning to ensure high IT-systems availability and it provides an end-to-end pipeline that ingests data from on-premises and cloud data sources and reports anomalous events to downstream monitoring and ticketing systems.
With this solution, you will quickly detect and fix issues based on underlying health metrics from IT infrastructure (CPU, memory, etc.), services (timeouts, SLA variations, brownouts, etc.) and other key performance indicators (order backlog, login and payment failures, etc.).
|This is the entry point of the pipeline, where the raw timeseries data is ingested.|
|Stream Analytics performs aggregation at 5-minute intervals and aggregates raw data points by metric name.|
|Azure Storage stores data aggregated by the Stream Analytics job.|
|Data Factory calls the Anomaly Detection API at regular intervals (every 15 minutes by default) on the data in Azure Storage. It stores the results in a SQL database.|
|SQL Database stores the results from the Anomaly Detection API, including binary detections and detection scores. It also stores optional metadata sent with the raw data points to allow for more complicated reporting.|
Machine Learning Studio
|This hosts the Anomaly Detection API. Note that the API itself is stateless and requires historical data points to be sent in each API call.|
|Detected anomalies are published to a service bus topic to enable consumption by external monitoring services.|
|Application Insights allows for monitoring of the pipeline.|
|Power BI provides dashboards showing the raw data, as well as detected anomalies.|
Related solution architectures
Learn how to use Azure ML to predict failures before they happen with real-time assembly line data.Learn more