Use case · Verified May 2026
ML training data without the PII
Real production data is the easiest way to train a model. It is also the easiest way to fail a privacy audit, leak a customer record into a model checkpoint, or block your team for six weeks waiting for legal sign-off.
Short answer
If you are pre-launch, blocked on legal review, or just want to iterate on model architecture without touching real data, SynthForge ships pre-built ML domain templates (healthcare, e-commerce, fraud, IoT, marketing, real estate) with configurable class balance, train/test split, and baseline accuracy/F1/R-squared metrics so you can iterate before the privacy review unblocks.
The situation
ML teams hit two recurring blockers. The first is regulatory: you cannot legally use the real customer data for the model you want to train, or you can but only after a long privacy review. The second is structural: you do not have the data yet, because the feature has not shipped.
Both blockers are unblocked the same way: by working against a representative synthetic dataset that is shaped like the real one without containing any of the real values.
What you do NOT want is to train a privacy-auditable production model on synthetic data that was generated without privacy guarantees. SynthForge is for the iteration phase: model architecture, feature engineering, baseline benchmarks. For privacy-grade synthetic data derived from real sensitive datasets, look at NVIDIA NeMo Safe Synthesizer (differential privacy) or Tonic Structural (de-identification). SynthForge does not do either.
How to do it in SynthForge
1. Pick a pre-built domain template, or describe a custom one
SynthForge ships templates for healthcare readmission, e-commerce churn, fraud detection, sensor anomaly, marketing conversion, and housing price prediction. Each template includes feature columns, a target column, realistic distributions, and class-balance defaults. Or describe your own schema in natural language and SynthForge will configure the target.
2. Tune class balance
Most real-world ML problems have skewed targets (1% fraud rate, 30% readmission rate). SynthForge lets you configure label_weights per target to match the real-world ratio. Default weights ship per template.
3. Generate with train/test split
Pick the train ratio (default 0.8). The generator stratifies by target for classification problems, randomly partitions for regression. Output is two CSV / Parquet files: train and test.
4. Read the baseline evaluation report
SynthForge trains a baseline model (majority class for classification, mean predictor + simple linear fit for regression) and reports accuracy, precision, recall, F1, and R-squared. Use this as your floor: any real model you train should beat the baseline by a meaningful margin.
5. Iterate against synthetic, then validate against real
Develop the architecture, the feature engineering, and the training pipeline against the synthetic dataset. When the privacy review unblocks, swap in the real dataset and re-train with the same code. Synthetic-trained models do not transfer perfectly, but they get you 80% of the way.
When something else is the right call
Honest alternatives in case SynthForge is not the best fit for your specific situation.
NVIDIA NeMo Safe Synthesizer (formerly Gretel Tabular)
When you have a real sensitive dataset, you need a privacy-preserving synthetic copy with differential-privacy guarantees, and you can wait hours for DP-SGD training. SynthForge does not do this; NeMo Safe Synthesizer is the right tool.
Tonic Structural
When you have a real production database with PII and you need a sanitized synthetic copy via masking, tokenization, format-preserving encryption, and NER-based de-identification.
Hugging Face datasets
When a public, real, already-anonymized benchmark dataset exists for your problem (MIMIC for healthcare, IEEE-CIS for fraud). Real beats synthetic when real is available and legal.
Frequently asked questions
Does SynthForge offer differential privacy?
Will a model trained on SynthForge data work on real data?
Can SynthForge handle a 3-way train / test / validation split?
What domain templates ship with SynthForge?
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