Navigating the Complexities of Generative AI Solutions for Structured Data Synthesis (intro)

A free session in cooperation with Tonic.ai

Tonic.ai is pioneering data de-identification, synthesis, and subsetting for modern data pipelines. Our products empower developers and data practitioners while protecting customer privacy by enabling companies to create safe, high-fidelity versions of their data for use in software development and testing, data analysis, and ML model training.

Speaker | Andrew Colombi, PhD, is the Co-founder and CTO of Tonic. An early employee at Palantir, he led the team of engineers that launched the company into the commercial sector and later began Palantir’s Foundry product. Today, he is building Tonic’s platform to engineer data that parallels the complex realities of modern data ecosystems and supply development teams with the resource-saving tools they most need.

Description

ChatGPT, Dall-E and Stable Diffusion are making waves, using generative AI to create synthetic unstructured data, but where is the equivalent solution for structured data, and what solutions exist today to get developers the realistic data they need to test and build their software? In this talk, we explore what makes applying generative AI techniques to structured data synthesis so challenging and what is possible in synthetic data generation today.

What we’ll cover

• Why realistic structured data is harder to generate than unstructured data
• The mathematics behind the challenges
• How unstructured generative AI works today, and how these techniques could be applied to structured data in the future

Who should join this session?

• Developers
• Testers and QA professionals
• DevOps managers
• Data analysts

Watch this course for free

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