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2026-02-14 18:58:01 -

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2026-02-14 18:58:01 -

Synthetic data is becoming essential for training models when real data is scarce, sensitive, or expensive to obtain. This tutorial goes beyond the basics—covering CTGAN with statistical validation and downstream utility testing, which is where most synthetic data projects actually succeed or fail. Useful if you're working with tabular data and need to maintain distribution fidelity

[In-Depth Guide] The Complete CTGAN + SDV Pipeline for High-Fidelity Synthetic Data

In this tutorial, we build a complete, production-grade synthetic data pipeline using CTGAN and the SDV ecosystem. We start from raw mixed-type tabular data and progressively move toward constrained generation, conditional sampling, statistical validation, and downstream utility testing. Rather than stopping at sample generation, we focus on understanding how well synthetic data preserves structure, distributions, […] The post [In-Depth Guide] The Complete CTGAN + SDV Pipeline for High-Fid

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