Sold by: MphasisÂ
Deployed on AWS
GenAI solution to generate synthetic data that preserves the properties of original data while ensuring 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. This solution generates synthetic tabular data, preserving privacy and matching original features while allowing unlimited rows. It enables analytics when data is scarce, using CS & KS tests for statistical validation and assessing privacy by comparing distances between synthetic and original data observations.
Highlights
- This solution supports single table tabular data. It can be used to generate synthetic data for industries like financial services, healthcare and retail.
- This solution can benefit in alternate sceanrios such as reducing data imbalance, unavailability of data, upsampling rare event data. It can help companies to protect privacy of data.
- PACE - ML is Mphasis Framework and Methodology for end-to-end machine learning development and deployment. PACE-ML enables organizations to improve the quality & reliability of the machine learning solutions in production and helps automate, scale, and monitor them. 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.m5.4xlarge 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 | $16.00 |
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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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
This is version 3.1
Additional details
Inputs
- Summary
input.csv is the input data file for which synthetic data is required.
- input_parameters. json consists of three parameters, i.e.,
- {"drop_cols": list of column which do not synthesize otherwise None "cat_cols": list of categorical columns, "factor": multiplicative factor correcosponding to no of observation, i.e., 0.5 will give half no of observation as compared to original no of records however 2 will give double no of observations }
- Provide data in mentoned format only
- input_parameters. json consists of three parameters, i.e.,
- Input MIME type
- text/csv
https://github.com/Mphasis-ML-Marketplace/Synthetic_tabular_data_generation/tree/main/input
https://github.com/Mphasis-ML-Marketplace/Synthetic_tabular_data_generation/tree/main/input
Input data descriptions
The following table describes supported input data fields for real-time inference and batch transform.
Field name | Description | Constraints | Required |
|---|---|---|---|
'csv' | Input should have one csv and json file | Type: FreeText | Yes |
Resources
Vendor resources
Support
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