Thanks to visit codestin.com Credit goes to sdv.dev
Kalyan Veeramachaneni
23 January, 2023
How we define a user in 2023 to build a community around synthetic data.
Neha Patki
26 January, 2023
Our open source grew faster when we adopted a user-centric mindset. Here are 3 strategies we used along the way.
10 January, 2023
Imbalanced data can prevent your projects from succeeding. Will synthetic data work? Explore the rationale behind label balancing.
Santiago Gomez Paz
20 December, 2022
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
Proper evaluation is critical when using synthetic data. 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.
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.
Let's put synthetic data to work.
