Sold by: MphasisÂ
Deployed on AWS
GenAI solution to generate relational synthetic data that preserves properties of original tables and their relationships with privacy.
Overview
Among Generative AI's most compelling applications is the generation of synthetic data, a process critical to overcoming the challenges of data privacy, scarcity, and imbalance. Central to this endeavor is SynthStudio, a sophisticated generative model designed to produce high-quality synthetic tabular data, reflecting the nuances of real-world datasets while ensuring privacy and enhancing data utility. Generating data instances that mimic the distribution of real datasets is achieved through advanced ML techniques, including Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Transformer models. The solution generates maximum two tables with parent and child relationship. It also provides statistical metrics to determine whether a variable is likely to come from a specified distribution and privacy metrics to evaluate the synthetic data generated. "
Highlights
- This offering is based on a deep learning approach. It can be utilized for any type of tabular data like financial transcations or healthcare records.
- This solution can be repurposed for scenarios such as reducing data imbalance or supplementing in case of unavailability or sparsity of data.
- 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.m5.4xlarge Inference (Batch) Recommended | Model inference on the ml.m5.4xlarge instance type, batch mode | $0.00 |
ml.m5.4xlarge Inference (Real-Time) Recommended | Model inference on the ml.m5.4xlarge instance type, real-time mode | $0.00 |
ml.m5.4xlarge Training Recommended | Algorithm training on the ml.m5.4xlarge instance type | $10.00 |
ml.m4.4xlarge Inference (Batch) | Model inference on the ml.m4.4xlarge instance type, batch mode | $0.00 |
ml.m4.16xlarge Inference (Batch) | Model inference on the ml.m4.16xlarge instance type, batch mode | $0.00 |
ml.m5.2xlarge Inference (Batch) | Model inference on the ml.m5.2xlarge instance type, batch mode | $0.00 |
ml.p3.16xlarge Inference (Batch) | Model inference on the ml.p3.16xlarge instance type, batch mode | $0.00 |
ml.m4.2xlarge Inference (Batch) | Model inference on the ml.m4.2xlarge instance type, batch mode | $0.00 |
ml.c5.2xlarge Inference (Batch) | Model inference on the ml.c5.2xlarge instance type, batch mode | $0.00 |
ml.p3.2xlarge Inference (Batch) | Model inference on the ml.p3.2xlarge instance type, batch mode | $0.00 |
Vendor refund policy
Currently we do not support refunds, but you can cancel your subscription to the service at any time
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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
This is version 1.0.0
Additional details
Inputs
- Summary
- parent_table_input.csv, child_table_input.csv are the original relational datasets
- parent_table_variable_info.json & child_table_variable_info.json should contain the variable type (ID, name, categorical, numerical) and variable sensitivity (True, False)
- input_parameters.json should contain the parent_table_drop_cols, child_table_drop_cols and scale factor (no. of rows in synthetic compared to original)
- relational_table_structure.json contains the relationship info between the tables
- Input MIME type
- application/zip
https://github.com/Mphasis-ML-Marketplace/Relational-Synthetic-Data-Generator/tree/main/input
https://github.com/Mphasis-ML-Marketplace/Relational-Synthetic-Data-Generator/tree/main/input
Resources
Vendor resources
Support
Vendor support
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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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