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.
Tabular Synthetic Data Generator
By:
Latest Version:
3.1
GenAI solution to generate synthetic data that preserves the properties of original data while ensuring privacy.
Product 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.
Key Data
Version
By
Type
Algorithm
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!
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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
Algorithm Training$16/hr
running on ml.m5.4xlarge
Model Realtime Inference$0.00/hr
running on ml.m5.4xlarge
Model Batch Transform$0.00/hr
running on ml.m5.4xlarge
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 Algorithm Training$0.922/host/hr
running on ml.m5.4xlarge
SageMaker Realtime Inference$0.922/host/hr
running on ml.m5.4xlarge
SageMaker Batch Transform$0.922/host/hr
running on ml.m5.4xlarge
Algorithm Training
For algorithm training 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 | Algorithm/hr | |
|---|---|---|
ml.m4.4xlarge | $16.00 | |
ml.c5n.18xlarge | $16.00 | |
ml.g4dn.4xlarge | $16.00 | |
ml.m5.4xlarge Vendor Recommended | $16.00 | |
ml.m4.16xlarge | $16.00 | |
ml.m5.2xlarge | $16.00 | |
ml.p3.16xlarge | $16.00 | |
ml.g4dn.2xlarge | $16.00 | |
ml.c5n.xlarge | $16.00 | |
ml.m4.2xlarge | $16.00 | |
ml.c5.2xlarge | $16.00 | |
ml.p3.2xlarge | $16.00 | |
ml.c4.2xlarge | $16.00 | |
ml.g4dn.12xlarge | $16.00 | |
ml.m4.10xlarge | $16.00 | |
ml.c4.xlarge | $16.00 | |
ml.m5.24xlarge | $16.00 | |
ml.c5.xlarge | $16.00 | |
ml.g4dn.xlarge | $16.00 | |
ml.p2.xlarge | $16.00 | |
ml.m5.12xlarge | $16.00 | |
ml.g4dn.16xlarge | $16.00 | |
ml.p2.16xlarge | $16.00 | |
ml.c4.4xlarge | $16.00 | |
ml.m5.xlarge | $16.00 | |
ml.c5.9xlarge | $16.00 | |
ml.m4.xlarge | $16.00 | |
ml.c5.4xlarge | $16.00 | |
ml.p3.8xlarge | $16.00 | |
ml.m5.large | $16.00 | |
ml.c4.8xlarge | $16.00 | |
ml.c5n.2xlarge | $16.00 | |
ml.p2.8xlarge | $16.00 | |
ml.g4dn.8xlarge | $16.00 | |
ml.c5n.9xlarge | $16.00 | |
ml.c5.18xlarge | $16.00 | |
ml.c5n.4xlarge | $16.00 |
Usage Information
Training
- You need "input_zip.zip" which contains input.csv and input_parameters.json.
- This is not a inference based listing.
- For each data set model has to be trained independently.
- It would provide synthetic data that will consists of same faeture set as in the original data.
Channel specification
Fields marked with * are required
training
*Input modes: File
Content types: text/csv, application/zip, application/x-zip-compressed
Compression types: None, Gzip
Model input and output details
Input
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 MIME type
text/csvSample input data
Output
Summary
Output will have two files.
- Output.csv - generated synthetic data
- performance.csv - Provide performance of synthetic data with respect to privacy and statistical similarity
In this listing, there is no inferencing required.
Output MIME type
text/csvSample output data
Sample notebook
Additional Resources
End User License Agreement
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Support Information
Tabular Synthetic Data Generator
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