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.

Synthetic Data Generation - SWIFT MT103
By:
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
Version
By
Type
Algorithm
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.
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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.
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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 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$0.23/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/plainSample 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/plainSample output data
Sample notebook
Additional Resources
End User License Agreement
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Support Information
Synthetic Data Generation - SWIFT MT103
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