Generating Realistic Synthetic Data with ChatGPT
Examine the photo above. It showcases a decorative well I came across near Bialystok in northeastern Poland. Despite the fact it does not work, it’s become a “sort of” tourist attraction. Some visitors, myself included (don’t ask me why), are drawn to photograph it. Its appeal lies not in its authenticity (nor aesthetics) but in its value as a point of interest.
Similarly, in this article, I’ll delve into another “faux” element with significant potential: synthetic data. Synthetic data is data produced algorithmically instead of being sourced from real-world events. It serves as a substitute for test data sets derived from production or operational data, and is employed to validate mathematical models and train machine learning (ML) tools.¹
I took my first writing assignments in January 2022. Later, I have branched into training and video blogs. I can see an apparent demand among those who read my articles or attend sessions: they want actionable insights. They’re not there for abstract theories; they want hands-on examples they can apply immediately. But here’s the challenge: crafting examples requires the correct data. It must be simple and complex enough for learners to reflect on real-world situations. And it must align perfectly with the topic at hand.
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