By identifying data points, events, or observations that deviate from a general pattern, we can enhance efficiency and predictability in a range of scenarios from infrastructure management to fraud detection. In this article, we will explore how to create a solution using Microsoft’s Anomaly Detector, a part of the Azure Cognitive Services suite.
This AI-powered service thoroughly scans your time-series data and ingests varying patterns to effectively spot anomalies with high precision. Also, this process can adapt to changing trends in the data over time. Anomaly Detector can execute anomaly detection in two modes: the Latest point anomaly detection mode and the Batch anomaly detection mode.
1. Latest point anomaly detection mode:
This mode works by sending a series of data points to the Anomaly Detector API, which detects if the latest point is an anomaly.
2. Batch anomaly detection mode:
This mode operates by sending a series of data points to the Anomaly Detector API, which scans through all the given points and labels each one as an anomaly or not.
Creating a solution with Anomaly Detector
In order to create an Anomaly Detector solution, one follows a series of steps as outlined below:
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Set Up Azure Subscription:
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Create Anomaly Detector Resource:
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Collect Anomaly Detector Resource Keys and Endpoint:
Implementing Azure AI services requires an active Azure subscription. If one is not in your possession, you can create a free account.
Go to the Azure Portal, and in your dashboard, proceed to ‘Create a resource’, search for ‘Anomaly Detector’, and click on create. Fill out the required form and create.
Navigate to your newly created Anomaly Detector resource in the Azure Portal. Under ‘Resource Management’, you will find keys and endpoint. Store them securely as you will be using them in your request to Anomaly Detector API.
To invoke anomaly detection through this service, you can use any HTTP client, for example, CURL or Postman, or use an SDK provided. Here’s an example of doing it through Python:
from azure.ai.anomalydetector import AnomalyDetectorClient
from azure.ai.anomalydetector.models import DetectRequest
from azure.core.credentials import AzureKeyCredential
import pandas as pd
endpoint = "
subscription_key = "
client = AnomalyDetectorClient(endpoint, AzureKeyCredential(subscription_key))
# Load some sample data
data = pd.read_csv('sample.csv', header=None, encoding='utf-8', parse_dates=[0])
request = DetectRequest(series=data, granularity='daily', max_anomaly_ratio=0.25, sensitivity=95)
# Detect anomalies in the entire series
response = client.detect_entire_series(request)
result = [res.is_anomaly for res in response.is_anomaly]
The detector client is used to send a request to the Anomaly Detector service. The DetectRequest class is used to build the request using the loaded data and specifying parameters such as the granularity of data, maximum anomaly ratio, and the sensitivity.
The response from the Anomaly Detector service, whether it’s in the batch detection mode or the latest detection mode, will contain a boolean field “isAnomaly” that indicates if the point is an anomaly based on the model’s calculations.
In summary, Microsoft’s Anomaly Detector, part of the Azure Cognitive Services, can provide effective and prescriptive anomaly detection solutions for your data analytics needs. Its flexible API can be geared to suit your specific requirements and create an environment conducive for superior predictive performance.
Practice Test
True or False: Anomaly Detector is a part of Microsoft’s Cognitive Services.
Answer: True
Explanation: Anomaly Detector is indeed a part of Microsoft’s Cognitive Services, offering a set of APIs to detect anomalies in time series data.
What is the primary purpose of the Anomaly Detector?
- A) To create new data
- B) To erase outdated data
- C) To identify unusual patterns or outliers in the data
- D) To cleanse data
Answer: C) To identify unusual patterns or outliers in the data
Explanation: Anomaly Detector’s main function is to identify unusual data patterns that may suggest a problem – otherwise known as anomalies.
True or False: The Anomaly Detector API allows you to monitor and detect abnormalities in your time series data.
Answer: True
Explanation: The Anomaly Detector API provides capabilities to track and identify anomalies in time series data, making it valuable for monitoring and troubleshooting.
In the context of Cognitive Services, what is a Time Series?
- A) A series of data points collected or recorded at uniform time intervals.
- B) A chronological sequence of qualitative data.
- C) An algorithm used in Anomaly Detector
- D) A Microsoft Azure subscription plan
Answer: A) A series of data points collected or recorded at uniform time intervals.
Explanation: A Time Series is a sequence of numerical data points collected at regular time intervals. Anomaly Detector works with this type of data.
True or False: Anomaly Detector does not require machine learning knowledge to use.
Answer: True
Explanation: One of the benefits of Anomaly Detector is that it encapsulates complex machine learning algorithms and allows developers to use it effectively without requiring expertise in machine learning.
Which services can be integrated with Anomaly Detector?
- A) Azure Machine Learning
- B) Azure Logic Apps
- C) Azure Stream Analytics
- D) All of the above
Answer: D) All of the above
Explanation: Anomaly Detector can be integrated with Azure Machine Learning, Azure Logic Apps, and Azure Stream Analytics among others, increasing its flexibility and use cases.
True or False: Anomaly Detector is capable of automatically discerning and adjusting to trends in data.
Answer: True
Explanation: Anomaly Detector uses advanced algorithms that automatically adapt to changes and trends in data, making it more accurate over time.
What are some possible use cases of Anomaly Detector?
- A) Fraud detection
- B) Monitoring IoT device data
- C) Monitoring business metrics
- D) All of the above
Answer: D) All of the above
Explanation: Anomaly Detector is applicable in a variety of scenarios, including fraud detection, monitoring IoT device data, and tracking business metrics.
True or False: The Anomaly Detector does not support batch anomaly detection.
Answer: False
Explanation: Anomaly Detector supports both batch and streaming anomaly detection, giving users the flexibility to choose the method that best suits their needs.
Who can benefit from using Anomaly Detector in their solutions?
- A) Data scientists
- B) Developers
- C) Business analysts
- D) All of the above
Answer: D) All of the above
Explanation: A variety of roles can benefit from using Anomaly Detector, including data scientists, developers, and business analysts, as it simplifies what would otherwise require complex programming or data science knowledge.
Interview Questions
What is the main function of the Anomaly Detector as part of the Cognitive Services in Azure?
The Anomaly Detector service in Azure Cognitive Services is used to detect abnormalities in time series data with machine learning technology. It adapts to changing trends in the data, thus continuing to find anomalies as the data evolves.
Where can Anomaly Detector be applied?
Anomaly Detector can be applied in various fields, including IT monitoring, fraud detection, business metrics tracking, and IoT device monitoring.
Does Anomaly Detector require a prior knowledge of machine learning to be used?
No, Anomaly Detector does not require prior machine learning knowledge. It is a pre-trained AI service that can be used via API calls.
What is the role of Time Series data in Anomaly Detector?
Time Series data is the main input for Anomaly Detector. It uses this data to identify trends, patterns, and anomalies.
How does the Anomaly Detector service in Azure handle changing trends in the data?
The Anomaly Detector uses an adaptive learning model. It continues to train and learn as new data points are added, allowing it to handle changes in trends over time.
What types of detection does the Anomaly Detector support?
The Anomaly Detector supports batch and real-time anomaly detection.
What kind of response does the Anomaly Detector API provide when detecting anomalies in time series data?
The API provides a response that includes information specific to each point in the time series data such as the detected anomalies and expected values.
Can Anomaly Detector handles multi-dimensional data?
Yes, the latest update has added support for multi-dimensional data in the Anomaly Detector.
Are there any specific data requirements for using Anomaly Detector?
Yes, the Anomaly Detector requires time series data with periodic patterns, and it expects the data to contain at least 12 data points.
How is Anomaly Detector different from traditional statistical methods?
Unlike traditional methods, Anomaly Detector can adapt to changing trends in data. It doesn’t require a pre-set threshold and can handle complexity in the data more efficiently.
Can Anomaly Detector be used to detect anomalies in IoT devices?
Yes, Anomaly Detector can be used to detect anomalies in IoT device data to identify potential device failures or malfunctions.
Does the Anomaly Detector provide explanations for the anomalies it detects?
Yes, each anomaly detected by Anomaly Detector comes with an explanation that provides insights into what made the data point anomalous.
How does the Anomaly Detector handle missing points or irregularly spaced points in a data series?
Azure Anomaly Detector handles missing and irregular points by interpolating the missing values.
Can I integrate Anomaly Detector with other Azure services?
Yes, Anomaly Detector can be integrated with other Azure services like Azure Data Factory, Azure Databricks for broader data analytics, and machine learning workflows.
What are the key components of the Anomaly Detector API response?
The key components include the ‘isAnomaly’ flag specifying whether the point is an anomaly, ‘expectedValue’ indicating what the value should have been according to the trend, and the ‘upperMargin’ and ‘lowerMargin’ providing the range where the value should lie.
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