Create a generative model for any dataset

Apply Synthesized Scientific Data Kit (SDK) to bootstrap data where the density of data is low, automatically rebalance data to improve model performance, and anonymise data for repurposing.
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<10 mins
Model performance improvement via automated data quality
To bootstrap data or rebalance the underlying dataset
Supported ML models and use cases
Automated data compliance framework
SDK on Cloud
Get up and running in minutes with Synthesized SDK fully managed on available marketplaces.
Synthesized SDK on GCPSynthesized SDK on Microsoft Azure
Synthesized SDK
Creates tables for machine learning and analytics.
pip install synthesized
Synthesized SDK

Optimized model performance

Improved model performance

Benefit from up to 15% uplift in model performance with data rebalancing, data imputation, and high-quality synthetic data generation. SDK helps increase revenue across conversion, fraud, revenue recovery, and more.

API-first extensible framework

Extend and plug-in into any data platform or ETL pipeline including Airflow, Dataproc, Spark. Fast and easy deployments using Kubernetes, OpenShift, and Docker.

Guaranteed compliance

"Data as Code" approach enables you to codify complex compliance requirements into concrete data transformations.

Full analytics and reporting

Full visibility of key data metrics including data quality, data compliance, and model performance metrics in your reports.

Data quality automation

Automate high-quality data creation using machine learning and common workflows.

Data rebalancing

Amplify the signal and reduce noise from original data. Multiple scenarios that allow for thorough model testing.

Data bootstrapping

Automatic data upsampling and bootstrapping for backtesting, cross-validation and more. Unlimited volumes of data.

Deep data imputation

Enable robust data imputation to increase model performance.
Accurately represent your intended population, resulting in more accurate and robust models.
Automatically generate accurate data points for datasets with missing values or outliers at scale.

Data compliance automation

Codifying data compliance requirements into concrete data transformations.
Generate required volumes of anonymous data from generative models.
Robustness against complex attacks, such as linkage attacks and attribute disclosure.
Configure data masking parameters to meet your organization’s needs.
Data compliance requirements verification and validation.
Python package available on MacOS, Windows & Linux.
Available in pre-built Docker images ready to deploy to your Kubernetes cluster.
First-party support for AWS, GCP, Azure.
Configurable to run on CPU and GPU.

Full integrations

Whether you are a data engineer, data scientist, or machine learning researcher, Synthesized SDK can be easily integrated into your existing workflows for ETL, data preparation, and model training. It’s all set and ready to use.
Handles any relational database/schemas
Integrates into any CI/CD and IDE
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Next steps

“Data as Code” approach makes it easy for anyone to be a data engineer.
Synthesized SDK
Creates tables for machine learning and analytics.
Read Tutorials
View workflow examples for Synthesized SDK.