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Deployed on AWS
Anomaly detection for time series data that exhibits cyclic or seasonal patterns
Overview
CAD (Contextual Anomaly Detection) is a technology that monitors and learns normal patterns for time-series data with specific patterns and detects abnormal patterns that deviate from the normal pattern. Unlike Point Anomaly Detection, it can identify anomaly patterns in time-series data even if they do not significantly exceed a certain upper or lower bound, as long as they differ from the normal pattern. Using CAD has several advantages for users. Firstly, it provides automated Hyperparameter Optimization (HPO), eliminating the need for users to search for the best parameters for modeling. Secondly, CAD has its own algorithms to improve anomaly detection performance, filtering abnormal data from normal data in the model. CAD offers users convenient settings such as the group key function, which automatically generates models based on the number of subgroups in the dataset, and the ability to switch on/off continual learning, which continuously updates the model with inference data.
Highlights
- Automated Hyperparameter Optimization (HPO) for detecting anomalies in pattern data.
- Providing algorithms that improve the performance of anomaly detection, not only by using machine learning models.
- User convenience functions including groupkey and continual learning.
Details
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Delivery method
Latest version
Deployed on AWS
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Features and programs
Financing for AWS Marketplace purchases
AWS Marketplace now accepts line of credit payments through the PNC Vendor Finance program. This program is available to select AWS customers in the US, excluding NV, NC, ND, TN, & VT.
Pricing
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/host/hour |
|---|---|---|
ml.m5.xlarge Inference (Real-Time) Recommended | Model inference on the ml.m5.xlarge instance type, real-time mode | $0.00 |
ml.m5.xlarge Training Recommended | Algorithm training on the ml.m5.xlarge instance type | $0.00 |
ml.m5.2xlarge Inference (Batch) Recommended | Model inference on the ml.m5.2xlarge instance type, batch mode | $0.00 |
ml.m4.4xlarge Inference (Real-Time) | Model inference on the ml.m4.4xlarge instance type, real-time mode | $0.00 |
ml.m5.4xlarge Inference (Real-Time) | Model inference on the ml.m5.4xlarge instance type, real-time mode | $0.00 |
ml.m5.12xlarge Inference (Real-Time) | Model inference on the ml.m5.12xlarge instance type, real-time mode | $0.00 |
ml.m4.16xlarge Inference (Real-Time) | Model inference on the ml.m4.16xlarge instance type, real-time mode | $0.00 |
ml.m5.2xlarge Inference (Real-Time) | Model inference on the ml.m5.2xlarge instance type, real-time mode | $0.00 |
ml.c4.4xlarge Inference (Real-Time) | Model inference on the ml.c4.4xlarge instance type, real-time mode | $0.00 |
ml.c5.9xlarge Inference (Real-Time) | Model inference on the ml.c5.9xlarge instance type, real-time mode | $0.00 |
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Delivery details
Amazon SageMaker algorithm
An Amazon SageMaker algorithm is a machine learning model that requires your training data to make predictions. Use the included training algorithm to generate your unique model artifact. Then deploy the model on Amazon SageMaker for real-time inference or batch processing. Amazon SageMaker is a fully managed platform for building, training, and deploying machine learning models at scale.
Deploy the model on Amazon SageMaker AI using the following options:
Version release notes
Add a hyperparameter configuration that allows users to set the x_column, time_column, and index_column of the input data.
Additional details
Inputs
- Summary
To train the model, you need to prepare the input data with three essential columns (time, value, and cycle) and set the hyperparameters using a Python dictionary. Check out the aia-cad-notebook guide about available setting arguments in hyperparameters for more information.
- Input MIME type
- text/csv
https://github.com/AI-Advisor-ML-Marketplace/contextual-anomaly-detection/blob/master/data/inference/test.csv
https://github.com/AI-Advisor-ML-Marketplace/contextual-anomaly-detection/blob/master/data/inference/test.csv
Input data descriptions
The following table describes supported input data fields for real-time inference and batch transform.
Field name | Description | Constraints | Required |
|---|---|---|---|
time | * time: time column(%Y-%m-%dT%H:%M:%S)
* value: target value
* cycle: column to distinguish the patterns in the data. Ensure that data within the same pattern is recorded with the same value. | Type: Continuous | Yes |
value | * time: time column(%Y-%m-%dT%H:%M:%S)
* value: target value
* cycle: column to distinguish the patterns in the data. Ensure that data within the same pattern is recorded with the same value. | Type: Continuous | Yes |
cycle | * time: time column(%Y-%m-%dT%H:%M:%S)
* value: target value
* cycle: column to distinguish the patterns in the data. Ensure that data within the same pattern is recorded with the same value. | Type: Continuous | Yes |
Resources
Vendor resources
Support
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
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