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Synthetic Data: Insights, updates and more
Learn more about synthetic data, our product and successful use cases
The Most Important Open Source Demographic That No One Thinks About
How we define a user in 2023 to build a community around synthetic data.
Can you use synthetic data for label balancing?
Imbalanced data can prevent your projects from succeeding. Will synthetic data work? Explore the rationale behind label balancing.
Interpreting the Progress of CTGAN
It can be difficult to verify the progress that a GAN is making. What if we combined it with easily interpretable metrics and visualizations?
How to evaluate synthetic data quality for your project — and avoid the biggest mistake we see
Evaluating synthetic data quality is critical. Avoid this common mistake and lead your project to success.
ML Model Development using Synthetic Data Clones
What happens when you train a machine learning model on synthetic data instead of real data? Let's experiment to find out.
Building the Unique Combinations Constraint in the SDV
Sometimes, you want to limit the amount of permutations in your synthetic data. Explore the strategies we used for enforcing this kind of logic.
The SDV in 2021: A year in review
In this article, we summarize SDV growth – downloads as well as community building – that indicates increasing market demand for synthetic data.
How we engineered constraint handling strategies in SDV
The SDV enforces deterministic rules using constraints. What strategies did we use to engineer this ML system? Dive into the details.
User input to enhance synthetic data generation
ML models learn some rules out of the box, while other logic requires more work. Which is which? Read more to find out.
Software Testing: Synthetic data changes the game
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.
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