The Synthpylon blog
Technical writing on synthetic data, privacy-safe ML, and tabular data engineering. We write what we wish existed when we were building the tool.
Why Real Data Blocks ML Teams (And What to Do About It)
Access controls, GDPR minimization, and HIPAA restrictions aren't edge cases. They stop entire ML pipelines. A frank look at the four most common blockages and the tradeoffs of each workaround.
What "Statistical Fidelity" Actually Means for Synthetic Data
Fidelity isn't just "does it look similar." We break down the three metrics that matter: marginal distributions, pairwise correlations, and downstream model accuracy delta.
The GDPR and HIPAA Gap in ML Training Data
Data minimization under GDPR and minimum necessary under HIPAA create the same problem: the data your model needs is exactly the data you can't touch.
Schema-Preserving Generation: Why Column Types Aren't Enough
A synthetic dataset that breaks foreign key constraints or generates negative ages is worse than useless. How Synthpylon handles referential integrity and type coherence.
How to Evaluate Synthetic Data Quality Before You Train
Most teams skip quality evaluation and wonder why their synthetic-data-trained model underperforms. A practical checklist: column distributions, pairwise correlations, TSTR benchmark.
Anonymization vs. Synthesis: They Are Not the Same Thing
Anonymized data is still derived from real records and can often be re-identified. Synthetic data is generated from scratch. The distinction matters legally and technically.
Building an ML Pipeline That Never Touches PII
A practical architecture walkthrough: schema ingestion at the boundary, synthetic generation in the middle tier, model training on clean synthetic output.
Tabular vs. Text Synthetic Data: Different Problems, Different Tools
The techniques for generating synthetic tabular records are fundamentally different from LLM-based text generation. Confusing them leads to wrong tool selection and poor results.
A Primer on Privacy-Safe Analytics with Synthetic Records
If you need to share aggregate insights from sensitive data without sharing the data itself, synthetic records can stand in. What works, what doesn't, and where the risk surface remains.
Using Synthetic Data to Oversample Minority Classes
SMOTE is a classic but operates on feature space only. Generating synthetic minority-class rows from a probabilistic model preserves joint distributions that SMOTE ignores.
How Much Accuracy Do You Lose by Training on Synthetic Data?
The honest answer: it depends on fidelity score. We ran train-on-synthetic, test-on-real benchmarks across five tabular classification tasks. Results, methodology, and what the gaps mean.
When to Use Synthetic Data (And When Not To)
Synthetic data solves specific problems: privacy constraints, class imbalance, data scarcity in dev/test. It doesn't replace real data for production model evaluation. A clear-eyed guide.