Synthesized + Google Cloud

As artificial intelligence, or AI, and machine learning is commoditized, data becomes an enterprise's competitive advantage. Accessing high-quality data is expensive, takes a long time, and sometimes is not even possible. Once obtained, data can be unbalanced, low in density, or of inadequate quality. Through AI-driven data transformations, Synthesized enables you to quickly access high-quality data to use in BI/Analytics, machine learning, application development, and testing workflows, in a compliant manner.

Synthesized Scientific Data Kit (SDK) is now available on Google Marketplace

Learn about the release in this video

The SDK helps you create compliant statistical-preserving data snapshots for BI/Analytics and ML/AI applications, and right-size your data with AI-driven data generation.

With the SDK, you can:

• 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.

• Enable fast data access and lower data acquisition cost - extract data insights faster for BI/Analytics. Increase developer productivity and speed-to-market.

• 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.

For Vertex AI applications, apply the SDK to bootstrap data where the density of data is low, automatically rebalance data to improve model performance, and anonymise data for repurposing.

For BigQuery, the SDK can sit on top of BigQuery to create secure snapshots for the end user, ensuring compliance with tools per enterprise regulations. Synthesized mimics the structure of data in the cloud and enables you to confidently add sensitive data.

Synthesized SDK is enabled on the marketplace via a Jupyter Notebook Server with the pre-installed SDK, providing an easy platform to start working with generative modeling. The Jupyter Notebook environment lets users create Python notebooks to load and process datasets, generate training/test data, and finally save the generated data to a desired destination.

Key Benefits:

• Increase market value of existing data
• Improve model performance by 4-15%
• Shorten model time to value from hours/days to minutes
• Increase developer productivity by 20%+
• 0% sensitive data leakage risk

Key Features:

• Data rebalancing
• Data snapshots
• Synthetic data generation
• Data anonymization
• Declarative Python DSL

Supported data types:

• Tabular data
• Time-series data
• Event-based data
• Multi-table
  

“Our partnership with Google Cloud enables companies across different industries to accelerate their digital transformation and adoption of the cloud. Together we have the expertise and know-how to enable companies to leverage their most sensitive data assets, in a fully compliant manner, to deliver true competitive advantage”, said Nicolai Baldin, CEO of Synthesized.