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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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Synthetic Data Generation - SWIFT MT103

Latest Version:
3.1
GenAI solution to generate MT103 SWIFT messages that mimics real SWIFT messages without compromising on privacy

    Product Overview

    "SynthStudio is a sophisticated Generative AI solution designed to produce high-quality synthetic data, reflecting the nuances of real data while ensuring privacy and overcoming the challenges of data privacy, scarcity, and imbalance. 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. Synthetic data for SWIFT MT103(Single Customer Credit Transfer) creates curated synthetic data which helps users to test their systems, do simulation exercises, train employees and do compliance testing without getting exposed to actual SWIFT messages. These synthetic swift messages mimic actual transactions in their format and structure. The synthetic data is generated for both the Mandatory and Optional tags. Users can select countries, currencies and banks of their choice and enter custom banks to help generate personalized data."

    Key Data

    Type
    Algorithm
    Fulfillment Methods
    Amazon SageMaker

    Highlights

    • This solution helps banks or other financial institutions to get access to synthetically generates SWIFT messages which exactly mimic actual SWIFT in terms of structure and content. The synthetically generated data conatins both Mandatory as well as optional fields.

    • The solution also gives users to get information of how a swift transaction would look like between certain countries, currencies and banks of their choice. Added to it users can enter custom banks for synthetic data generation.

    • 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.

    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$10/hr

    running on ml.m5.xlarge

    Model Realtime Inference$0.00/hr

    running on ml.m5.large

    Model Batch Transform$0.00/hr

    running on ml.m5.xlarge

    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$0.23/host/hr

    running on ml.m5.xlarge

    SageMaker Realtime Inference$0.115/host/hr

    running on ml.m5.large

    SageMaker Batch Transform$0.23/host/hr

    running on ml.m5.xlarge

    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
    $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
    Vendor Recommended
    $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

    Channel specification

    Fields marked with * are required

    training

    *
    Input modes: File
    Content types: application/zip, application/gzip
    Compression types: None, Gzip

    Model input and output details

    Input

    Summary

    The user input is a input_zip.zip, which has two files: 1) user_input.json: It is a json file where keys are 'countries', 'IBAN', 'currencies', 'no_of_datapoints'. The values in the fields are the filters for the above mentioned fields. 2) more_banks.csv: If user wants more banks than what is available, then the bank information could be provided in a .csv format where the fields are: 'ISO COUNTRY CODE', 'COUNTRY NAME' , 'INSTITUTION NAME', 'IBAN BIC', 'ADDRESS_BANK' , 'CURRENCY'.

    Limitations for input type
    In the user_input.json, the 'no_of_datapoints' field is mandatory, the user has to specify how much datapoints are required in the synthetic data. Rest all are optional
    Input MIME type
    application/zip, application/gzip, text/plain
    Sample input data

    Output

    Summary

    The model output is a output.json file which contains list of json strings, where each json string is a synthetic sample SWIFT MT103 message.

    Output MIME type
    application/gzip, application/zip, text/plain
    Sample output data

    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

    Synthetic Data Generation - SWIFT MT103

    For any assistance reach out to us at:

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    Refund Policy

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