Neha Patki
10 January, 2023
Imbalanced data can prevent your projects from succeeding. Will synthetic data work? Explore the rationale behind label balancing.
07 October, 2022
Evaluating synthetic data quality is critical. Avoid this common mistake and lead your project to success.
Arnav Modi
24 February, 2022
What happens when you train a machine learning model on synthetic data instead of real data? Let's experiment to find out.
25 January, 2022
Sometimes, you want to limit the amount of permutations in your synthetic data. Explore the strategies we used for enforcing this kind of logic.
Kalyan Veeramachaneni
03 January, 2022
In this article, we summarize SDV growth – downloads as well as community building – that indicates increasing market demand for synthetic data.
Andrew Montanez
21 December, 2021
The SDV enforces deterministic rules using constraints. What strategies did we use to engineer this ML system? Dive into the details.
01 December, 2021
ML models learn some rules out of the box, while other logic requires more work. Which is which? Read more to find out.
16 November, 2021
Creating fake data is an old concept -- but machine learning is a whole new ballgame. Learn about why ML is a key ingredient to synthetic data.
19 May, 2021
After thousands of downloads, see how the synthetic data workflow in the SDV has evolved based on feedback from users.
12 May, 2021
After thousands of downloads, see how SDV's machine learning models have evolved based on feedback from users.
Neha Patki and Carles Sala
04 May, 2021
Welcome to the SDV Blog! The SDV is a comprehensive, open source software for synthetic data generation. Join our growing community as we create an ecosystem to solve real world problems!
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