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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.

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Tabular Data Synthesizer

By:
Synthesized
Latest Version:
2.8.0
Generate high quality, privacy-preserving datasets for Machine Learning and Data Science.
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    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
    2.8.0
    Usage Instructions
    Release Notes
    By
    Synthesized
    Categories
    Generative AI
    Data Preparation
    Masking/Tokenization
    Type
    Algorithm
    Fulfillment Methods
    Amazon SageMaker

    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

    Not quite sure what you’re looking for? AWS Marketplace can help you find the right solution for your use case. Contact us

    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 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.

    1. train: this channel references a directory containing a training dataset in CSV format.

    2. 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/json
    Sample input data
    Expand all input descriptions
    Field name
    num_rows
    Description
    Integer specifying the number of rows of data to generate. If not supplied, the synthetic data generated will have the same number of rows as the original.
    Required
    No
    Data type
    Integer
    Range
    Min: 1
    Default value
    Number of rows of original DataFrame
    Field name
    produce_nans
    Description
    Whether to generate synthetic data with missing values in the same proportion as the original data.
    Required
    No
    Data type
    FreeText
    Default value
    "true"
    Limitations
    One of "true" or "false".
    Field name
    rebalance
    Description
    A list of dictionaries with keys describing specifiying column names along with a desired output distribution. An example for a 50-50 split of true/false for a column named "fraud_column" is given below: [{ "name": fraud_column, "marginals":{ "false": 0.5 "true": 0.5 } }]
    Required
    No
    Data type
    FreeText
    Default value
    []
    Limitations
    The sum of the marginals must be equal to 1.0.

    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/csv
    Sample output data
    Expand all output descriptions
    Field name
    Output DataFrame
    Description
    Follows the same format as the training data.
    Always returned
    Yes
    Data type
    FreeText

    Sample notebook

    Additional Resources

    End User License Agreement

    By subscribing to this product you agree to terms and conditions outlined in the product End user License Agreement (EULA)

    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 More

    Refund Policy

    Please contact support@synthesized.io.

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