Sold by: MeerKATÂ
Deployed on AWS
Multivariate Anomaly detection for time series data that exhibits cyclic or seasonal patterns
Overview
MAD stands for Multivariate Anomaly Detection, which is a technology that learns and monitors normal patterns for time-series data with specific patterns and can detect abnormal patterns that deviate from the normal pattern. Unlike Point Anomaly Detection, it identifies anomaly patterns in the 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 MAD has many advantages for users. Firstly, it provides automated Hyperparameter Optimization(HPO), so users don't have to find the best parameters for modeling. Secondly, MAD has its own algorithms to improve the anomaly detection performance, which filters the abnormal data from normal data in the model.Highlights
- Optimal window size setting for detecting anomalies in pattern data with multi-variable
- Optimal anomaly-decision-threshold setting to minimize false alarm
- Use state-of-the-art deep learning technique
Details
Sold by
Categories
Delivery method
Latest version
Deployed on AWS
Unlock automation with AI agent solutions
Fast-track AI initiatives with agents, tools, and solutions from AWS Partners.
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.c5.2xlarge Training Recommended | Algorithm training on the ml.c5.2xlarge instance type | $0.00 |
ml.c5.4xlarge Inference (Batch) Recommended | Model inference on the ml.c5.4xlarge instance type, batch mode | $0.00 |
ml.c5.2xlarge Inference (Real-Time) Recommended | Model inference on the ml.c5.2xlarge instance type, real-time mode | $0.00 |
ml.m4.4xlarge Training | Algorithm training on the ml.m4.4xlarge instance type | $0.00 |
ml.m5.4xlarge Training | Algorithm training on the ml.m5.4xlarge instance type | $0.00 |
ml.m5.12xlarge Training | Algorithm training on the ml.m5.12xlarge instance type | $0.00 |
ml.m4.16xlarge Training | Algorithm training on the ml.m4.16xlarge instance type | $0.00 |
ml.m5.2xlarge Training | Algorithm training on the ml.m5.2xlarge instance type | $0.00 |
ml.c4.4xlarge Training | Algorithm training on the ml.c4.4xlarge instance type | $0.00 |
ml.m5.xlarge Training | Algorithm training on the ml.m5.xlarge instance type | $0.00 |
Vendor refund policy
This product is offered for free. If there are any questions, please contact us for further clarifications.
Legal
Vendor terms and conditions
Upon subscribing to this product, you must acknowledge and agree to the terms and conditions outlined in the vendor's End User License Agreement (EULA)Â .
Content disclaimer
Vendors are responsible for their product descriptions and other product content. AWS does not warrant that vendors' product descriptions or other product content are accurate, complete, reliable, current, or error-free.
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
You need to input time series unstructured data in csv format. Specify the time and target columns in the hyperparameters. The data index must be at least 300. Below is the method for setting hyperparameters.
- Input MIME type
- text/csv
https://github.com/AI-Advisor-ML-Marketplace/multivariate-nomaly-detection/blob/master/data/inference/test.csv
https://github.com/AI-Advisor-ML-Marketplace/multivariate-nomaly-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 |
|---|---|---|---|
timestamp |
timestamp: time format data(UTC format)
value1, value2, value3, ... : target columns(multi-column) | Type: Continuous | Yes |
value1 |
timestamp: time format data(UTC format)
value1, value2, value3, ... : target columns(multi-column) | Type: Continuous | Yes |
value2 |
timestamp: time format data(UTC format)
value1, value2, value3, ... : target columns(multi-column) | Type: Continuous | Yes |
value3 |
timestamp: time format data(UTC format)
value1, value2, value3, ... : target columns(multi-column) | 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.
Similar products
Jama Connect® is an award-winning and leading solution for requirements management, providing robust traceability from requirements through testing and risk management. Jama Connect enables a digital thread across best-of-breed tools, allowing the product development process to be measured and improved.
An AI-powered analytics platform that offers pre-built, extensive, state-of-the-art solution to overcome the most common Analytics requirements of an insurance company.
Organizations in every industry are seeking ways to leverage AI but often don't know how to get started and what use cases are appropriate, or lack technology and data science skills. Ironside's Ascent.AI was designed to de-risk AI adoption and lift organizations over these hurdles with our pre-integrated environment, highly skilled data science team, and best practice methodology. The Rapid Viability Assessment is a 2 week module in which the data quality is explored and the machine learning viability evaluated.
Ankercloud’s ESC Readiness Assessment helps organizations evaluate their cloud environment, identify compliance gaps, and prepare for a smooth transition to the European Sovereign Cloud. As an ESC launch partner, Ankercloud ensures your business is ready to migrate when the region goes live in Q4 2025, aligning with strict EU data protection and sovereignty standards.
Customer reviews
No customer reviews yet
Be the first to review this product . We've partnered with PeerSpot to gather customer feedback. You can share your experience by writing or recording a review, or scheduling a call with a PeerSpot analyst.