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.
Relational Synthetic Data Generator
By:
Latest Version:
v1.0.0
GenAI solution to generate relational synthetic data that preserves properties of original tables and their relationships with 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. 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. "
Key Data
Version
By
Categories
Type
Algorithm
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.
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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$10/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 | $10.00 | |
ml.c5n.18xlarge | $10.00 | |
ml.g4dn.4xlarge | $10.00 | |
ml.m5.4xlarge Vendor Recommended | $10.00 | |
ml.m4.16xlarge | $10.00 | |
ml.m5.2xlarge | $10.00 | |
ml.p3.16xlarge | $10.00 | |
ml.g4dn.2xlarge | $10.00 | |
ml.c5n.xlarge | $10.00 | |
ml.m4.2xlarge | $10.00 | |
ml.c5.2xlarge | $10.00 | |
ml.p3.2xlarge | $10.00 | |
ml.c4.2xlarge | $10.00 | |
ml.g4dn.12xlarge | $10.00 | |
ml.m4.10xlarge | $10.00 | |
ml.c4.xlarge | $10.00 | |
ml.m5.24xlarge | $10.00 | |
ml.c5.xlarge | $10.00 | |
ml.g4dn.xlarge | $10.00 | |
ml.p2.xlarge | $10.00 | |
ml.m5.12xlarge | $10.00 | |
ml.g4dn.16xlarge | $10.00 | |
ml.p2.16xlarge | $10.00 | |
ml.c4.4xlarge | $10.00 | |
ml.m5.xlarge | $10.00 | |
ml.c5.9xlarge | $10.00 | |
ml.m4.xlarge | $10.00 | |
ml.c5.4xlarge | $10.00 | |
ml.p3.8xlarge | $10.00 | |
ml.m5.large | $10.00 | |
ml.c4.8xlarge | $10.00 | |
ml.c5n.2xlarge | $10.00 | |
ml.p2.8xlarge | $10.00 | |
ml.g4dn.8xlarge | $10.00 | |
ml.c5n.9xlarge | $10.00 | |
ml.c5.18xlarge | $10.00 | |
ml.c5n.4xlarge | $10.00 |
Usage Information
Training
- You need "input.zip" which contains parent_table_input.csv, child_table_input.csv, parent_table_variable_info.json, child_table_variable_info.json, input_parameters.json, relational_table_structure.json
- This is not a inference based listing.
- It would provide synthetic data for each of the original tables while maintaining the primary key relationship.
Channel specification
Fields marked with * are required
training
*Input modes: File
Content types: application/zip, application/x-zip-compressed
Compression types: None, Gzip
Model input and output details
Input
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/zipSample input data
Output
Summary
- parent_table.csv and child_table.csv are the synthetic datasets generated
- eval_parent_table.csv and eval_child_table.csv are the performance metrics in regards to privacy and statistical similarity
- In this listing, there is no inferencing required
Output MIME type
application/jsonSample output data
Sample notebook
Additional Resources
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Support Information
Relational Synthetic Data Generator
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