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From Expectations to Synthetic Data Generation

Posted Aug 20, 2022 | Views 890
# Synthetic Data
# Leverage
# Data Management
# YData.ai
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Fabiana Clemente
Co-founder & Chief Data Officer @ YData

Fabiana has been leading data science teams in multinational companies and startups. She has an academic background in Applied Maths, and MSc in Data Management combined with other degrees in Deep Learning and Secure and Private AI. As YData’s Co-Founder & CDO, she combines Data Understanding, Causality, and Privacy as her main fields of work and research, with the mission to make data actionable for organizations.

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Demetrios Brinkmann
Chief Happiness Engineer @ MLOps Community

At the moment Demetrios is immersing himself in Machine Learning by interviewing experts from around the world in the weekly MLOps.community meetups. Demetrios is constantly learning and engaging in new activities to get uncomfortable and learn from his mistakes. He tries to bring creativity into every aspect of his life, whether that be analyzing the best paths forward, overcoming obstacles, or building lego houses with his daughter.

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Ben Epstein
Founding Software Engineer @ Galileo

Ben was the machine learning lead for Splice Machine, leading the development of their MLOps platform and Feature Store. He is now a founding software engineer at Galileo (rungalileo.io) focused on building data discovery and data quality tooling for machine learning teams. Ben also works as an adjunct professor at Washington University in St. Louis teaching concepts in cloud computing and big data analytics.

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SUMMARY

Synthetic data is a hot topic in the current Data/ML space, being proposed as an essential feature in any Data Science toolkit. In a very hands-on approach, the objective is to showcase and depict how to generate synthetic data, deal with the challenges of leveraging deep learning networks for the process, and overcome them in a workflow that includes data profiling and the definition of expectations for the data generation.

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