Create a solution that uses Anomaly Detector, part of Cognitive Services

With the increasing need for anomaly detection in various industries, Microsoft has introduced Anomaly Detector, a powerful tool in the Cognitive Services suite of Azure AI solutions. Anomaly Detector provides developers with the ability to detect abnormalities and anomalies in time-series data, allowing them to build intelligent applications that can identify and respond to unexpected patterns.

One of the key features of Anomaly Detector is its ability to automatically learn the normal behavior of the time-series data provided, providing a baseline for anomaly detection. This eliminates the need for manual intervention in defining thresholds or rules for anomaly detection, making it a highly efficient and scalable solution.

Getting Started with Anomaly Detector

To get started with Anomaly Detector, you need to create a Cognitive Services resource in the Azure portal. Once the resource is created, you can obtain the endpoint and access key, which are required for authentication. You’ll also need to install the Anomaly Detector client library, which is available for various programming languages.

Let’s take a look at a Python example to demonstrate how to use Anomaly Detector for anomaly detection:

import os
from azure.ai.anomalydetector import AnomalyDetectorClient
from azure.core.credentials import AzureKeyCredential

# Process the response
if response.is_anomaly:
print("Anomaly detected!")
else:
print("No anomaly detected.")


In the code above, we first authenticate with the Anomaly Detector service using the endpoint URL and the subscription key. Next, we define the time-series data as a list of JSON objects, where each object represents a data point with a timestamp and a corresponding value. We then call the detect_entire_series method to detect anomalies in the entire time-series.

The response from the service indicates whether an anomaly is detected or not. Based on the response, you can implement appropriate actions in your application. For example, you could trigger an alert, log the anomaly for analysis, or take corrective actions to address the anomaly.

Batch and Streaming Detection Modes

Anomaly Detector supports both batch and streaming detection modes. Batch detection allows you to detect anomalies in a historical set of time-series data, while streaming detection enables real-time anomaly detection on incoming data. You can choose the mode that best suits your application’s needs.

Additional Capabilities

In addition to anomaly detection, Anomaly Detector also provides capabilities for forecasting and anomaly explanations. The forecasting feature allows you to predict future values based on historical data, and the anomaly explanations feature provides insights into why a particular point in the time-series is flagged as an anomaly.

In conclusion, Anomaly Detector is a valuable tool for developers looking to incorporate anomaly detection capabilities into their applications. By leveraging the power of Azure AI, you can build intelligent systems that proactively identify anomalous patterns in time-series data, enabling timely responses and improved decision-making.