Azure SDK for Java Reference Documentation
Azure Anomaly Detector client library for Java
Anomaly Detector is an AI service with a set of APIs, which enables you to monitor and detect anomalies in your time series data with little machine learning (ML) knowledge, either batch validation or real-time inference.
Source code | Package (Maven) | API reference documentation | Product Documentation | Samples
Getting started
Prerequisites
- A Java Development Kit (JDK), version 11 or later.
- Azure Subscription
- An existing Cognitive Services or Anomaly Detector resource.
For more information about creating the resource or how to get the location and sku information see here.
Include the Package
<dependency>
<groupId>com.azure</groupId>
<artifactId>azure-ai-anomalydetector</artifactId>
<version>3.0.0-beta.5</version>
</dependency>
Authenticate the client
In order to interact with the Anomaly Detector service, you'll need to create an instance of the AnomalyDetectorClient class. You will need an endpoint and an API key to instantiate a client object.
Get API Key
You can obtain the endpoint and API key from the resource information in the Azure Portal.
Alternatively, you can use the Azure CLI snippet below to get the API key from the Anomaly Detector resource.
az cognitiveservices account keys list --resource-group <your-resource-group-name> --name <your-resource-name>
Create AnomalyDetectorClient with Azure Active Directory Credential
You can authenticate with Azure Active Directory using the Azure Identity library. Note that regional endpoints do not support AAD authentication. Create a custom subdomain for your resource in order to use this type of authentication.
To use the DefaultAzureCredential provider shown below, or other credential providers provided with the Azure SDK, please include the azure-identity package:
<dependency>
<groupId>com.azure</groupId>
<artifactId>azure-identity</artifactId>
<version>1.5.4</version>
</dependency>
You will also need to register a new AAD application and grant access to Anomaly Detector by assigning the "Cognitive Services User" role to your service principal.
Set the values of the client ID, tenant ID, and client secret of the AAD application as environment variables: AZURECLIENTID, AZURETENANTID, AZURECLIENTSECRET.
Sync client
String endpoint = Configuration.getGlobalConfiguration().get("AZURE_ANOMALY_DETECTOR_ENDPOINT");
String key = Configuration.getGlobalConfiguration().get("AZURE_ANOMALY_DETECTOR_API_KEY");
AnomalyDetectorClient anomalyDetectorClient =
new AnomalyDetectorClientBuilder()
.credential(new AzureKeyCredential(key))
.endpoint(endpoint)
.buildClient();
Key concepts
With the Anomaly Detector, you can either detect anomalies in one variable using Univariate Anomaly Detection, or detect anomalies in multiple variables with Multivariate Anomaly Detection.
|Feature |Description | |---------|---------| |Univariate Anomaly Detection | Detect anomalies in one variable, like revenue, cost, etc. The model was selected automatically based on your data pattern. | |Multivariate Anomaly Detection| Detect anomalies in multiple variables with correlations, which are usually gathered from equipment or other complex system. The underlying model used is Graph attention network.|
Univariate Anomaly Detection
The Univariate Anomaly Detection API enables you to monitor and detect abnormalities in your time series data without having to know machine learning. The algorithms adapt by automatically identifying and applying the best-fitting models to your data, regardless of industry, scenario, or data volume. Using your time series data, the API determines boundaries for anomaly detection, expected values, and which data points are anomalies.
Using the Anomaly Detector doesn't require any prior experience in machine learning, and the REST API enables you to easily integrate the service into your applications and processes.
With the Univariate Anomaly Detection, you can automatically detect anomalies throughout your time series data, or as they occur in real-time.
|Feature |Description | |---------|---------| | Streaming detection| Detect anomalies in your streaming data by using previously seen data points to determine if your latest one is an anomaly. This operation generates a model using the data points you send, and determines if the target point is an anomaly. By calling the API with each new data point you generate, you can monitor your data as it's created. | | Batch detection | Use your time series to detect any anomalies that might exist throughout your data. This operation generates a model using your entire time series data, with each point analyzed with the same model. | | Change points detection | Use your time series to detect any trend change points that exist in your data. This operation generates a model using your entire time series data, with each point analyzed with the same model. |
Multivariate Anomaly Detection
The Multivariate Anomaly Detection APIs further enable developers by easily integrating advanced AI for detecting anomalies from groups of metrics, without the need for machine learning knowledge or labeled data. Dependencies and inter-correlations between up to 300 different signals are now automatically counted as key factors. This new capability helps you to proactively protect your complex systems such as software applications, servers, factory machines, spacecraft, or even your business, from failures.
With the Multivariate Anomaly Detection, you can automatically detect anomalies throughout your time series data, or as they occur in real-time. There are three processes to use Multivariate Anomaly Detection.
- Training: Use Train Model API to create and train a model, then use Get Model Status API to get the status and model metadata.
- Inference:
- Use Async Inference API to trigger an asynchronous inference process and use Get Inference results API to get detection results on a batch of data.
- You could also use Sync Inference API to trigger a detection on one timestamp every time.
- Other operations: List Model API and Delete Model API are supported in Multivariate Anomaly Detection model for model management.
Thread safety
We guarantee that all client instance methods are thread-safe and independent of each other (guideline). This ensures that the recommendation of reusing client instances is always safe, even across threads.
Examples
The following section provides several code snippets covering some of the most common Anomaly Detector service tasks, including:
- Univariate Anomaly Detection - Batch detection
- Univariate Anomaly Detection - Streaming detection
- Univariate Anomaly Detection - Detect change points
- Multivariate Anomaly Detection
Create client
String endpoint = Configuration.getGlobalConfiguration().get("AZURE_ANOMALY_DETECTOR_ENDPOINT");
String key = Configuration.getGlobalConfiguration().get("AZURE_ANOMALY_DETECTOR_API_KEY");
AnomalyDetectorClient anomalyDetectorClient =
new AnomalyDetectorClientBuilder()
.credential(new AzureKeyCredential(key))
.endpoint(endpoint)
.buildClient();
Batch detection
For batch detection in univariate anomaly detection, please go to this sample for better understanding the workflow: DetectAnomaliesEntireSeries.java
Streaming detection
For streaming/last detection in univariate anomaly detection, please go to this sample for better understanding the workflow: DetectAnomaliesLastPoint.java
Detect change points
For change points detection in univariate anomaly detection, please go to this sample for better understanding the workflow: DetectChangePoints.java
Multivariate Anomaly Detection Sample
To see how to use Anomaly Detector library to conduct Multivariate Anomaly Detection, see this MultivariateSample.java.
Troubleshooting
Enabling Logging
Azure SDKs for Java offer a consistent logging story to help aid in troubleshooting application errors and expedite their resolution. The logs produced will capture the flow of an application before reaching the terminal state to help locate the root issue. View the logging wiki for guidance about enabling logging.
Next steps
These code samples show common scenario operations with the Azure Anomaly Detector library. More samples can be found under the samples directory.
For more extensive documentation on Azure Anomaly Detector, see the Anomaly Detector documentation on docs.microsoft.com.
Contributing
This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit cla.microsoft.com.
When you submit a pull request, a CLA-bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., label, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.
This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.