Azure Ai Anomalydetector Java
Azure Ai Anomalydetector Java is an data AI skill with a core value of Build anomaly detection applications with Azure AI Anomaly Detector SDK for Java. It
helps developers solve real-world problems in the data domain, boosting
efficiency, automating repetitive tasks, and optimizing workflows.
Build anomaly detection applications with Azure AI Anomaly Detector SDK for Java. Use when implementing univariate/multivariate anomaly detection, time-series analysis, or AI-powered monitoring.
Quick Facts
mkdir -p ./skills/azure-ai-anomalydetector-java && curl -sfL https://raw.githubusercontent.com/sickn33/antigravity-awesome-skills/main/skills/azure-ai-anomalydetector-java/SKILL.md -o ./skills/azure-ai-anomalydetector-java/SKILL.mdRun in terminal / PowerShell. Requires curl (Unix) or PowerShell 5+ (Windows).
Skill Content
# Azure AI Anomaly Detector SDK for Java
Build anomaly detection applications using the Azure AI Anomaly Detector SDK for Java.
Installation
<dependency>
<groupId>com.azure</groupId>
<artifactId>azure-ai-anomalydetector</artifactId>
<version>3.0.0-beta.6</version>
</dependency>Client Creation
Sync and Async Clients
import com.azure.ai.anomalydetector.AnomalyDetectorClientBuilder;
import com.azure.ai.anomalydetector.MultivariateClient;
import com.azure.ai.anomalydetector.UnivariateClient;
import com.azure.core.credential.AzureKeyCredential;
String endpoint = System.getenv("AZURE_ANOMALY_DETECTOR_ENDPOINT");
String key = System.getenv("AZURE_ANOMALY_DETECTOR_API_KEY");
// Multivariate client for multiple correlated signals
MultivariateClient multivariateClient = new AnomalyDetectorClientBuilder()
.credential(new AzureKeyCredential(key))
.endpoint(endpoint)
.buildMultivariateClient();
// Univariate client for single variable analysis
UnivariateClient univariateClient = new AnomalyDetectorClientBuilder()
.credential(new AzureKeyCredential(key))
.endpoint(endpoint)
.buildUnivariateClient();With DefaultAzureCredential
import com.azure.identity.DefaultAzureCredentialBuilder;
MultivariateClient client = new AnomalyDetectorClientBuilder()
.credential(new DefaultAzureCredentialBuilder().build())
.endpoint(endpoint)
.buildMultivariateClient();Key Concepts
Univariate Anomaly Detection
- **Batch Detection**: Analyze entire time series at once
- **Streaming Detection**: Real-time detection on latest data point
- **Change Point Detection**: Detect trend changes in time series
Multivariate Anomaly Detection
- Detect anomalies across 300+ correlated signals
- Uses Graph Attention Network for inter-correlations
- Three-step process: Train → Inference → Results
Core Patterns
Univariate Batch Detection
import com.azure.ai.anomalydetector.models.*;
import java.time.OffsetDateTime;
import java.util.List;
List<TimeSeriesPoint> series = List.of(
new TimeSeriesPoint(OffsetDateTime.parse("2023-01-01T00:00:00Z"), 1.0),
new TimeSeriesPoint(OffsetDateTime.parse("2023-01-02T00:00:00Z"), 2.5),
// ... more data points (minimum 12 points required)
);
UnivariateDetectionOptions options = new UnivariateDetectionOptions(series)
.setGranularity(TimeGranularity.DAILY)
.setSensitivity(95);
UnivariateEntireDetectionResult result = univariateClient.detectUnivariateEntireSeries(options);
// Check for anomalies
for (int i = 0; i < result.getIsAnomaly().size(); i++) {
if (result.getIsAnomaly().get(i)) {
System.out.printf("Anomaly detected at index %d with value %.2f%n",
i, series.get(i).getValue());
}
}Univariate Last Point Detection (Streaming)
UnivariateLastDetectionResult lastResult = univariateClient.detectUnivariateLastPoint(options);
if (lastResult.isAnomaly()) {
System.out.println("Latest point is an anomaly!");
System.out.printf("Expected: %.2f, Upper: %.2f, Lower: %.2f%n",
lastResult.getExpectedValue(),
lastResult.getUpperMargin(),
lastResult.getLowerMargin());
}Change Point Detection
UnivariateChangePointDetectionOptions changeOptions =
new UnivariateChangePointDetectionOptions(series, TimeGranularity.DAILY);
UnivariateChangePointDetectionResult changeResult =
univariateClient.detectUnivariateChangePoint(changeOptions);
for (int i = 0; i < changeResult.getIsChangePoint().size(); i++) {
if (changeResult.getIsChangePoint().get(i)) {
System.out.printf("Change point at index %d with confidence %.2f%n",
i, changeResult.getConfidenceScores().get(i));
}
}Multivariate Model Training
import com.azure.ai.anomalydetector.models.*;
import com.azure.core.util.polling.SyncPoller;
// Prepare training request with blob storage data
ModelInfo modelInfo = new Mod🎯 Best For
- UI designers
- Product designers
- Claude users
- Data professionals
- Analytics teams
💡 Use Cases
- Generating component mockups
- Creating design system tokens
- Data pipeline auditing
- Query optimization
📖 How to Use This Skill
- 1
Install the Skill
Copy the install command from the Terminal tab and run it. The SKILL.md file downloads to your local skills directory.
- 2
Load into Your AI Assistant
Open Claude and reference the skill. Paste the SKILL.md content or use the system prompt tab.
- 3
Apply Azure Ai Anomalydetector Java to Your Work
Provide context for your task — paste source material, describe your audience, or share existing work to guide the AI.
- 4
Review and Refine
Edit the AI output for accuracy, tone, and completeness. Add human insight where the AI lacks context.
❓ Frequently Asked Questions
Does this work with Figma?
Some design skills integrate with Figma plugins. Check the Works With section for supported tools.
How do I install Azure Ai Anomalydetector Java?
Copy the install command from the Terminal tab and run it. The skill downloads to ./skills/azure-ai-anomalydetector-java/SKILL.md, ready to use.
Can I customize this skill for my team?
Absolutely. Edit the SKILL.md file to add team-specific instructions, examples, or workflows.
⚠️ Common Mistakes to Avoid
Skipping usability testing
AI-generated designs should be validated with real users before development.
Ignoring data quality
AI analysis inherits all data quality issues — profile your data first.