Open-source Tools for Synthetic Data On-Demand


Consider the challenges you could tackle if you could remove the three most common bottlenecks to modern data workflows – limited, low-quality, and unsafe data. Advanced synthetics enable you to generate high-fidelity, artificial data on-demand from limited samples, as well as turn existing sensitive datasets into secure, shareable resources that are provably private by design.

In this workshop, we’ll walk you through several real-world use cases for synthetic data. You’ll learn how to balance a biased medical dataset to improve early cancer detection in women, generate realistic time-series financial data for forecasting, and more. You can test the examples yourself – some with Gretel-synthetics, a fully open-source package, and some using Gretel Blueprints, a collection of notebooks and sample code that leverage the open-source package through Gretel’s client.


Lipika Ramaswamy is a Senior Applied Scientist at where she focuses on developing advanced synthetic data generation technologies that include privacy guarantees. Prior to, she worked as a data scientist at LeapYear, a differential privacy software company. Lipika attended Bryn Mawr College for her undergrad, where she began her STEM career, and holds a Master’s in Data Science from Harvard University.

Open Data Science




Open Data Science
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Cambridge, MA 02142

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