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HN mostly links its post on synthetic data for data science, which commenters working with synthetic data cite; it also appears in job postings.
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Full Stack Software Engineer-Angular/Typescript/Javascript
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I’d argue you’re incorrect on most accounts. I work in this space, and have implemented what’s being described related to synthetic data.
You can see some of my work here:
https://medium.com/capital-one-tech/why-you-dont-necessarily-need-data-for-data-science-48d7bf503074
The reality is, you can guarantee certain values are not passed. That’s fairly easy, what you can’t do is block general trends easily in synthetic data - else the model can’t learn. So you have to be willing to accept that “leakage” when using synthetic data.
If you accept that leakage, then you can actually improve model performance in some domains. See ubers architecture search blog, there’s a lot of material (also from Uber) showing this.
Regarding cost of training, yes model training costs could increase. Although, I’d suggest much much less than 10x more like 50% or something. Is that worth it for privacy?
I've been reading about D3 and am thinking about integrating it into my React project. But I've come across a number of posts that discuss how React & D3 don't play nicely together because they both manipulate the DOM. [1] is a good starting point for this deep dive.
Does Observable Plot resolve those issues or do we need still to be careful integrating it into a React environment?
[1] https://medium.com/capital-one-tech/a-comparison-of-data-visualization-libraries-for-react-27f8fe409934
I've actually done work on synthetic data development --
https://medium.com/capital-one-tech/why-you-dont-necessarily-need-data-for-data-science-48d7bf503074
Generally, we use it to avoid utilizing 'real' data. Accuracy usually is the same using either synthetic or real data. There are edge cases where one fails due to particular issues about how synthetic data suppresses outliers, etc.