Sold by: MphasisÂ
Deployed on AWS
This solution is a deep learning-based trainable algorithm, capable of detecting anomalous behavior in IoT sensor data.
Overview
This solution is a deep learning-based approach to learn and understand the patterns in IoT sensor data. It aims at learning the normal behavior patterns of the sensor data during training process using generative algorithms. Once trained, the model can identify abnormal signals from the sensor and classify them as anomalous.
Highlights
- Imbalanced data is a major challenge in anomaly detection domain, with huge non-anomalous data and limited anomalous data. This solution is capable of handling data imbalance and is a semi-supervised approach which uses generative deep learning algorithms. It learns normal IoT sensor patterns using non-anomalous data and builds a 1-rule threshold model using data from both classes. It then identifies the anomalous behavior of the sensor using inclusion-exclusion principle. The solution is also re-trainable to capture information drift.
- This solution is capable of handling huge amounts of class imbalance and capturing anomalous behavior. This makes the solution usable in multiple industries for predictive maintenance to raise early alarms indicating system’s abnormal behavior. Specific use cases could be production machine maintenance, IoT sensor data analysis for anomalous behavior identification, IT infrastructure maintenance, data drift detection.
- InfraGraf is a patented Cognitive infrastructure automation platform that optimizes enterprise technology infrastructure investments. It diagnoses and predicts infrastructure failures. Need customized Machine Learning and Deep Learning solutions? Get in touch!
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.large Inference (Batch) Recommended | Model inference on the ml.m5.large instance type, batch mode | $20.00 |
ml.m5.large Inference (Real-Time) Recommended | Model inference on the ml.m5.large instance type, real-time mode | $10.00 |
ml.m5.large Training Recommended | Algorithm training on the ml.m5.large instance type | $10.00 |
ml.m4.4xlarge Inference (Batch) | Model inference on the ml.m4.4xlarge instance type, batch mode | $20.00 |
ml.m5.4xlarge Inference (Batch) | Model inference on the ml.m5.4xlarge instance type, batch mode | $20.00 |
ml.m4.16xlarge Inference (Batch) | Model inference on the ml.m4.16xlarge instance type, batch mode | $20.00 |
ml.m5.2xlarge Inference (Batch) | Model inference on the ml.m5.2xlarge instance type, batch mode | $20.00 |
ml.p3.16xlarge Inference (Batch) | Model inference on the ml.p3.16xlarge instance type, batch mode | $20.00 |
ml.m4.2xlarge Inference (Batch) | Model inference on the ml.m4.2xlarge instance type, batch mode | $20.00 |
ml.c5.2xlarge Inference (Batch) | Model inference on the ml.c5.2xlarge instance type, batch mode | $20.00 |
Vendor refund policy
Currently we do not support refunds, but you can cancel your subscription to the service at any time.
Legal
Vendor terms and conditions
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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
bug fixes
Additional details
Inputs
- Summary
There should not be any categorical or string columns. Try to incorporate as much patterns from non-fraudulent data as possible to increase out of sample accuracy.
- Input MIME type
- application/zip, text/csv, text/plain, application/json
https://github.com/Mphasis-ML-Marketplace/Anomaly-Detection-in-IoT-Data/blob/main/data/training/train.csv
https://github.com/Mphasis-ML-Marketplace/Anomaly-Detection-in-IoT-Data/tree/main/data
Input data descriptions
The following table describes supported input data fields for real-time inference and batch transform.
Field name | Description | Constraints | Required |
|---|---|---|---|
train | Kindly visit the sample input data link. | Type: Continuous | Yes |
Resources
Vendor resources
Support
Vendor support
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