Amazon Sagemaker
Amazon SageMaker is a fully-managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. With Amazon SageMaker, all the barriers and complexity that typically slow down developers who want to use machine learning are removed. The service includes models that can be used together or independently to build, train, and deploy your machine learning models.
Structured Synthetic Data Evaluator
By:
Latest Version:
1.3
This solution evaluates tabular synthetic data against original data provided across various statistical similarity and privacy metrics
Product 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.
Key Data
Version
By
Categories
Type
Model Package
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.
Not quite sure what you’re looking for? AWS Marketplace can help you find the right solution for your use case. Contact us
Pricing Information
Use this tool to estimate the software and infrastructure costs based your configuration choices. Your usage and costs might be different from this estimate. They will be reflected on your monthly AWS billing reports.
Contact us to request contract pricing for this product.
Estimating your costs
Choose your region and launch option to see the pricing details. Then, modify the estimated price by choosing different instance types.
Version
Region
Software Pricing
Model Realtime Inference$5.00/hr
running on ml.m5.large
Model Batch Transform$10.00/hr
running on ml.m5.large
Infrastructure PricingWith Amazon SageMaker, you pay only for what you use. Training and inference is billed by the second, with no minimum fees and no upfront commitments. Pricing within Amazon SageMaker is broken down by on-demand ML instances, ML storage, and fees for data processing in notebooks and inference instances.
Learn more about SageMaker pricing
With Amazon SageMaker, you pay only for what you use. Training and inference is billed by the second, with no minimum fees and no upfront commitments. Pricing within Amazon SageMaker is broken down by on-demand ML instances, ML storage, and fees for data processing in notebooks and inference instances.
Learn more about SageMaker pricing
SageMaker Realtime Inference$0.115/host/hr
running on ml.m5.large
SageMaker Batch Transform$0.115/host/hr
running on ml.m5.large
Model Realtime Inference
For model deployment as Real-time endpoint in Amazon SageMaker, the software is priced based on hourly pricing that can vary by instance type. Additional infrastructure cost, taxes or fees may apply.InstanceType | Realtime Inference/hr | |
|---|---|---|
ml.m4.4xlarge | $5.00 | |
ml.m5.4xlarge | $5.00 | |
ml.m4.16xlarge | $5.00 | |
ml.m5.2xlarge | $5.00 | |
ml.p3.16xlarge | $5.00 | |
ml.m4.2xlarge | $5.00 | |
ml.c5.2xlarge | $5.00 | |
ml.p3.2xlarge | $5.00 | |
ml.c4.2xlarge | $5.00 | |
ml.m4.10xlarge | $5.00 | |
ml.c4.xlarge | $5.00 | |
ml.m5.24xlarge | $5.00 | |
ml.c5.xlarge | $5.00 | |
ml.p2.xlarge | $5.00 | |
ml.m5.12xlarge | $5.00 | |
ml.p2.16xlarge | $5.00 | |
ml.c4.4xlarge | $5.00 | |
ml.m5.xlarge | $5.00 | |
ml.c5.9xlarge | $5.00 | |
ml.m4.xlarge | $5.00 | |
ml.c5.4xlarge | $5.00 | |
ml.p3.8xlarge | $5.00 | |
ml.m5.large Vendor Recommended | $5.00 | |
ml.c4.8xlarge | $5.00 | |
ml.p2.8xlarge | $5.00 | |
ml.c5.18xlarge | $5.00 |
Usage Information
Model input and output details
Input
Summary
The input is a zip file containing the following files: 1) original data 2) synthetic data 3) 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/zipSample input data
Output
Summary
output.json contains list of metric scores for both statistical and privacy evaluation:
Output MIME type
application/jsonSample output data
Sample notebook
Additional Resources
End User License Agreement
By subscribing to this product you agree to terms and conditions outlined in the product End user License Agreement (EULA)
Support Information
Structured Synthetic Data Evaluator
For any assistance reach out to us at:
AWS Infrastructure
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