What are Decision Support Services?
Azure Decision Support services within Cognitive Services help developers integrate sophisticated reasoning and recommendation engines into their applications. These services analyze data, identify patterns, and provide actionable insights to drive informed decision-making.
Leveraging machine learning and AI, Decision Support services can:
- Personalize user experiences with tailored recommendations.
- Optimize operational efficiency by identifying best courses of action.
- Mitigate risks by predicting potential issues and suggesting preventative measures.
- Automate complex decision processes, reducing manual intervention.
Key Decision Support Capabilities
Anomaly Detector
Identify and diagnose potential issues in time-series data, flagging unexpected deviations.
Content Moderator
Automatically review text and images for offensive or unwanted content, ensuring a safe user environment.
Personalizer
Create personalized experiences by learning user preferences and delivering the most relevant content or actions.
Content Safety (Preview)
A next-generation service to help detect potentially harmful content across text, images, and video.
Getting Started with Decision Support
Integrating these powerful services into your applications is straightforward. You can use REST APIs or SDKs available in various programming languages.
Example: Using Anomaly Detector API (Conceptual)
Below is a simplified representation of how you might interact with the Anomaly Detector API. In a real application, you would handle authentication, error handling, and data serialization.
import requests
import json
endpoint = "YOUR_ANOMALY_DETECTOR_ENDPOINT"
api_key = "YOUR_ANOMALY_DETECTOR_API_KEY"
headers = {
"Content-Type": "application/json",
"Ocp-Apim-Subscription-Key": api_key
}
data = {
"series": [
{"timestamp": "2023-01-01T00:00:00Z", "value": 10},
{"timestamp": "2023-01-01T01:00:00Z", "value": 12},
{"timestamp": "2023-01-01T02:00:00Z", "value": 15},
{"timestamp": "2023-01-01T03:00:00Z", "value": 50}, # Potential anomaly
{"timestamp": "2023-01-01T04:00:00Z", "value": 11}
],
"granularity": "hourly",
"sensitivity": 90
}
response = requests.post(f"{endpoint}/anomalydetector/v1.0/timeseries/last/detect", headers=headers, data=json.dumps(data))
if response.status_code == 200:
result = response.json()
print(json.dumps(result, indent=2))
else:
print(f"Error: {response.status_code} - {response.text}")
This example demonstrates sending time-series data to the service to detect anomalies. The response would indicate which points in the series are flagged as anomalous.
Resources and Documentation
Dive deeper into Azure Decision Support services with the following resources: