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 Data Synthesizer
By:
Latest Version:
2.8.0
Generate high quality, privacy-preserving datasets for Machine Learning and Data Science.
Product Overview
The Tabular Data Synthesizer by Synthesized brings the generative AI capabilities of Synthesized's Scientific Data Kit (SDK) to AWS Sagemaker. Synthesized provides a comprehensive framework for generative modelling for structured data. The SDK helps you create compliant statistical-preserving data snapshots for BI/Analytics and ML/AI applications.
Key Data
Version
Type
Algorithm
Highlights
Improve data quality - benefit from up to ~15% uplift in ML/AI model performance with data rebalancing, data imputation, and high-quality synthetic data generation. SDK helps increase revenue across conversion, fraud, revenue recovery, and more.
Ensure data privacy and data compliance - codify complex data privacy requirements into concrete data transformations. Ensure compliance when using sensitive data in cloud initiatives. Rapidly migrate your data pipelines and workflows to the cloud faster.
Key Benefits:
- Increase market value of existing data
- Improve model performance by up to 15%
- Shorten model time to value from hours/days to minutes
- Increase developer productivity by 20%+
- Codified data privacy transformations for compliance
Key Features:
- Data rebalancing
- Data snapshots
- Synthetic data generation
- Data anonymisation
- JSON/YAML Configuration
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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$4.75/hr
running on ml.m5.2xlarge
Model Realtime Inference$4.75/hr
running on ml.m5.2xlarge
Model Batch Transform$4.75/hr
running on ml.m5.2xlarge
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$0.461/host/hr
running on ml.m5.2xlarge
SageMaker Realtime Inference$0.461/host/hr
running on ml.m5.2xlarge
SageMaker Batch Transform$0.461/host/hr
running on ml.m5.2xlarge
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.c5.2xlarge | $4.75 | |
ml.p3.2xlarge | $8.50 | |
ml.m5.2xlarge Vendor Recommended | $4.75 | |
ml.c5.18xlarge | $8.50 | |
ml.c5.xlarge | $4.25 | |
ml.m5.xlarge | $4.25 | |
ml.c5.9xlarge | $6.75 |
Usage Information
Training
The tabular synthesizer algorithm takes two channels of training data.
train
: this channel references a directory containing a training dataset in CSV format.config
(optional): this channel references a directory containing the configuration of the synthesizer in the form of a JSON file. It can be used to configure and override the publically available functionality of the SDK. Please see the documentation for more details.
Examples are provided in the accompanying demo notebook.
Metrics
Name | Regex |
---|---|
total_loss | total_loss: (.*?) - |
regularization_loss | regularization_loss: (.*?) - |
step_time | - (.*?)m?s/step |
epoch | Epoch (.*?)/ |
Channel specification
Fields marked with * are required
train
*The original tabular data source to be synthesized.
Input modes: File
Content types: text/csv
Compression types: None
config
JSON configuration file(s) for the synthesizer.
Input modes: File
Content types: application/json
Compression types: None
Hyperparameters
Fields marked with * are required
num_steps
The number of training steps to run. If unset, the model will use early stopping.
Type: Integer
Tunable: No
batch_size
The size of each training batch.
Type: Integer
Tunable: Yes
capacity
A reference number of dimensions for sizing the generative model.
Type: Integer
Tunable: Yes
latent_size
The number of dimensions of the models latent space.
Type: Integer
Tunable: Yes
Model input and output details
Input
Summary
The model input is a JSON config specifying the number of rows of data to generate along with some optional modifications. Please see our the synthesis section of our documentation for more details on the schema of the generation config.
Input MIME type
application/jsonSample input data
Output
Summary
The output of the model is a CSV file which follows the same schema of the data that it was trained on. When the endpoint is called in a notebook, the output can be converted to a Pandas DataFrame.
Output MIME type
text/csvSample output data
Sample notebook
Additional Resources
End User License Agreement
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Support Information
Tabular Data Synthesizer
For any questions, please contact support@synthesized.io or submit a ticket in the Synthesized support portal.
AWS Infrastructure
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.
Learn MoreRefund Policy
Please contact support@synthesized.io.
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