Datacebo provides a generative AI platform for creating synthetic tabular data for enterprise use.

DataCebo

Datacebo

DataCebo is an AI-powered synthetic data platform for enterprise. The Synthetic Data Vault libraries were originally created by MIT's Data-to-AI Lab after years of groundbreaking machine learning research.

Testing applications that support cashless payments can be challenging. This is due to frequent changes in the standards and new requirements that come up from time to time. Europe’s cashless standard SEPA, for example, is subject to frequent modifications that require applications processing these payments undergo regular & comprehensive testing. 

ING Belgium trained a synthetic-data model using real SEPA payments, such that it could then generate any amount of synthetic SEPA payments that follow the same structural and mathematical properties as the original.

Learn more from Neha Patki, our VP of Product, and wim blommaert, Head of Test Data Management at ING Belgium!

https://lnkd.in/emC4jCVM 

#generativeai #machinelearning #syntheticdata

Tune in to hear the keynote address from Kalyan Veeramachaneni, Co-founder DataCebo and founder MIT Data-to-AI Lab, "How Synthetic data created using Generative AI can help you competitively?"

Tune in to hear the keynote address from Kalyan Veeramachaneni, Co-founder DataCebo, and founder MIT Data-to-AI Lab, "How Synthetic data created using Generative AI can help you competitively?"

Most enterprises store data across multiple tables, with each table storing data for an entity and its attributes. Each single table captures just one part of a complex pattern. 

For example, as Neha Patki and Frances Hartwell explain, a customer's age may correlate with the amount of money they spent on a purchase. Even though the "age" and "amount per purchase" columns are present in different tables, the connection between the tables means that intertable trends exist. To fully understand these trends, and emulate how an entity and its data connect and evolve, a generative model must work across tables and learn these patterns. 

We at the The Synthetic Data Vault developed our first multi-table approach — popularly known as a Hierarchical Modeling Algorithm (HMA) — in 2016. Over the years, we have improved it and created many different generative modeling techniques for multi-table data. Each of our approaches strikes a delicate balance between modeling speed vs. how exhaustively the model learns the patterns, affecting the usability of the tool and the quality of the #syntheticdata generated, and letting users find an approach that matches their needs. 

Read our primer on multi-table synthesizers and the SDV on our blog: https://lnkd.in/etjqnJ9f.

As always, we learn and evolve based on your feedback. 
Join our Slack:  https://lnkd.in/ePnJgHhc
Try out SDV Community: https://docs.sdv.dev/sdv
Send us a request for SDV Enterprise:  https://lnkd.in/e97Ce89i

An important milestone for #generativeai for tabular data: CTGAN has reached 1000 citations!

CTGAN was originally published in NeurIPS 2019 and quickly became our most popular synthesizer. One of its earliest uses was changing the game in data science competitions. Before CTGAN, competition data was made up of real data that had been masked, anonymized, and otherwise adjusted for public release. CTGAN allowed Kaggle and Wells Fargo to create synthetic data sets for competitions, and the rest is history!

We're glad for this opportunity to remember CTGAN's origins, and celebrate its ongoing (and growing) role in the field. Thank you to the hundreds of researchers who have put CTGAN to work!

CTGAN is now part of The Synthetic Data Vault library, which you can try here: https://lnkd.in/dy5PMfE

Join our Slack community to learn how we are evolving to bring #generativeai for enterprises: https://lnkd.in/ePnJgHhc

#syntheticdata #kaggle #generativeai #ctgan

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