Sold by: MphasisÂ
Deployed on AWS
This solution evaluates tabular synthetic data against original data provided across various statistical similarity and privacy metrics
Overview
Synthetic Data is pertinent to deriving business insights without compromising on privacy. Synthetic Data Evaluation is a process to audit your synthetic data for ensuring high utility and privacy in downstream usecases. The evaluation is done on attribute similarity (statistical similarity, tabular correlation difference), model detection metrics (ability to segregate synthetic and original data points), missing levels in categorical attributes, privacy at risk data points and sensitive data retrieval (adversarial attack easiness). This solution provides an assessment of your provided synthetic structured data on the basis of these metrics.
Highlights
- This solution supports single table structured data evaluation. It can be used to evaluate synthetic data for industries like financial services, healthcare and retail.
- The solution helps in measuring attribute similarity, data driven discovery, missing levels in categorical attributes, privacy at risk data points, sensitive data retrieval. This allows companies to decide on data sharing for further downstream processes.
- Mphasis Synth Studio is an Enterprise Synthetic Data Platform for generating high-quality synthetic data that can help derive and monetize trustworthy business insights, while preserving privacy and protecting data subjects. Build reliable and high accuracy models when no or low data is available.
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 | $10.00 |
ml.m5.large Inference (Real-Time) Recommended | Model inference on the ml.m5.large instance type, real-time mode | $5.00 |
ml.m4.4xlarge Inference (Batch) | Model inference on the ml.m4.4xlarge instance type, batch mode | $10.00 |
ml.m5.4xlarge Inference (Batch) | Model inference on the ml.m5.4xlarge instance type, batch mode | $10.00 |
ml.m4.16xlarge Inference (Batch) | Model inference on the ml.m4.16xlarge instance type, batch mode | $10.00 |
ml.m5.2xlarge Inference (Batch) | Model inference on the ml.m5.2xlarge instance type, batch mode | $10.00 |
ml.p3.16xlarge Inference (Batch) | Model inference on the ml.p3.16xlarge instance type, batch mode | $10.00 |
ml.m4.2xlarge Inference (Batch) | Model inference on the ml.m4.2xlarge instance type, batch mode | $10.00 |
ml.c5.2xlarge Inference (Batch) | Model inference on the ml.c5.2xlarge instance type, batch mode | $10.00 |
ml.p3.2xlarge Inference (Batch) | Model inference on the ml.p3.2xlarge instance type, batch mode | $10.00 |
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
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 model
An Amazon SageMaker model package is a pre-trained machine learning model ready to use without additional training. Use the model package to create a 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
Version 1.3
Additional details
Inputs
- Summary
The input is a zip file containing the following files:
- original data
- synthetic data
- metadata mentioning non-sensitive/key fields and sensitive fields.
Please note the metadata information is mandatory for deriving the sensitive data retrieval test metric.
- Limitations for input type
- The sensitive and non-sensitive fields should be of same type (both are continuous variables).
- Input MIME type
- application/zip, text/plain
https://github.com/Mphasis-ML-Marketplace/Structured-Synthetic-Data-Evaluation/blob/main/input/Input.zip
https://github.com/Mphasis-ML-Marketplace/Structured-Synthetic-Data-Evaluation/blob/main/input/Input.zip
Resources
Vendor resources
Support
Vendor support
For any assistance reach out to us at:
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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