mostly-ai/mostlyai

What it solves

It addresses the challenge of accessing high-fidelity data for analysis and development while maintaining privacy. It allows users to create synthetic versions of tabular or language datasets that preserve the statistical properties of the original data without exposing sensitive information.

How it works

The SDK provides a programmatic interface to train "Generators" on existing data assets. These generators use various models—such as TabularARGN for tabular data, DNNs for graph relations, and fine-tuned Hugging Face models or LSTMs for text—to learn the underlying patterns of the data. Once trained, these generators can be used to create synthetic datasets, probe for specific representative samples, or perform conditional simulations based on fixed column values.

Who it’s for

It is designed for data scientists and developers who need privacy-safe datasets for testing, training ML models, or sharing data across organizations without compromising security.

Highlights

• Broad Data Support: Handles mixed-type data (categorical, numerical, geospatial, text), multi-table structures, and time-series.
• Flexible Deployment: Offers a LOCAL mode for on-premise compute and a CLIENT mode for remote endpoints.
• Advanced Sampling: Supports up-sampling, conditional simulations, re-balancing underrepresented segments, and statistical fairness controls.
• Quality Assurance: Includes automated quality metrics for fidelity and privacy, providing detailed HTML reports for visual analysis.
• Extensive Integration: Connects to various data sources including BigQuery, Snowflake, PostgreSQL, MySQL, and Oracle.