Overview
This solution efficiently creates high-quality tabular synthetic data. It empowers businesses and researchers to overcome data scarcity, privacy, and compliance challenges by generating realistic and representative synthetic datasets. It maintains the statistical properties, correlations, and patterns of the original data, ensuring the output remains useful and relevant for your use case. The synthetic data generator supports a wide range of data types, including numerical, categorical, and datetime variables, allowing you to generate synthetic data tailored to your specific needs.
Highlights
- The Synthetic Tabular Data Generataor uses generative adversarial networks to create synthetic data that accurately mimics the statistical properties of real data without revealing sensitive information, enabling compliance with GDPR, HIPAA, and other privacy regulations. Its efficient implementation ensures rapid generation of large-scale synthetic data, helping users save time and resources.
- This solution can be used by businesses, data science teams and software testing teams in various industries like healthcare, finance, retail, HR & workforce insurance and smart cities etc to complement their existing data scources in a reliable and privacy preserving manner.
- Need more machine learning, deep learning, NLP and Quantum Computing solutions. Reach out to us at Harman DTS.
Details
Categories
Delivery method
Latest version
Deployed on AWS
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Features and programs
Financing for AWS Marketplace purchases
AWS Marketplace now accepts line of credit payments through the PNC Vendor Finance program. This program is available to select AWS customers in the US, excluding NV, NC, ND, TN, & VT.
Pricing
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/host/hour |
|---|---|---|
ml.m5.large Inference (Batch) Recommended | Model inference on the ml.m5.large instance type, batch mode | $300.00 |
ml.m4.4xlarge Inference (Batch) | Model inference on the ml.m4.4xlarge instance type, batch mode | $300.00 |
ml.m5.4xlarge Inference (Batch) | Model inference on the ml.m5.4xlarge instance type, batch mode | $300.00 |
ml.m4.16xlarge Inference (Batch) | Model inference on the ml.m4.16xlarge instance type, batch mode | $300.00 |
ml.m5.2xlarge Inference (Batch) | Model inference on the ml.m5.2xlarge instance type, batch mode | $300.00 |
ml.p3.16xlarge Inference (Batch) | Model inference on the ml.p3.16xlarge instance type, batch mode | $300.00 |
ml.m4.2xlarge Inference (Batch) | Model inference on the ml.m4.2xlarge instance type, batch mode | $300.00 |
ml.c5.2xlarge Inference (Batch) | Model inference on the ml.c5.2xlarge instance type, batch mode | $300.00 |
ml.p3.2xlarge Inference (Batch) | Model inference on the ml.p3.2xlarge instance type, batch mode | $300.00 |
ml.c4.2xlarge Inference (Batch) | Model inference on the ml.c4.2xlarge instance type, batch mode | $300.00 |
Vendor refund policy
We do not provide any usage related refunds at this time.
Legal
Vendor terms and conditions
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Delivery details
Amazon SageMaker model
An Amazon SageMaker model package is a pre-trained machine learning model ready to use without additional training. Use the model package to create a model on Amazon SageMaker for real-time inference or batch processing. Amazon SageMaker is a fully managed platform for building, training, and deploying machine learning models at scale.
Deploy the model on Amazon SageMaker AI using the following options:
Version release notes
Feature updates and bug fixes
Additional details
Inputs
- Summary
The model take two files in a zipped file.
- A config file (config.txt) with two parameters
- "length_of_sample" : number of records to be generated (an integer)
- "categorical column": a comma separated list of column hearders in the sample csv file to be designated as categorical data type and treated as such during data generation
- A csv file containing the representative sample data to be used as a reference for synthetic data generation
- Limitations for input type
- Realtime inferencing is not supported due to the nature of use-case
- Input MIME type
- application/zip
https://github.com/HDTS-user/lifeware-synthetic-tabular-data-generator/tree/main/input
https://github.com/HDTS-user/lifeware-synthetic-tabular-data-generator/tree/main/input
Resources
Vendor resources
Support
Vendor support
Business hours email support marketplaceSupp@harman.comÂ
AWS infrastructure support
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.
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