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26 January, 2023
3 user-centric growth strategies for open source
Our open source grew faster when we adopted a user-centric mindset. Here are 3 strategies we used along the way.
23 January, 2023
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.
10 January, 2023
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.
20 December, 2022
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?
07 October, 2022
How to evaluate synthetic data for your project — and avoid the biggest mistake we see
Proper evaluation is critical when using synthetic data. Avoid this common mistake and lead your project to success.
24 February, 2022
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.
25 January, 2022
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.
03 January, 2022
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.
21 December, 2021
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.
01 December, 2021
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.