ML Engineering

Training on production data creates regulatory exposure your model cards won't save you from.

Generate privacy-safe training datasets without compromising statistical fidelity.

Regulatory context

The frameworks your auditors will cite.

GDPR Right to Erasure

If personal data is in your training set, erasure requests create a model retraining obligation, unless training data was properly anonymised.

GDPR Data Minimisation

Only data necessary for the specific purpose should be processed. Production data used for ML training typically violates this principle.

ICO AI and Data Protection

ICO guidance on lawful bases for training AI systems and the requirements for data minimisation in ML pipelines.

In practice

What ML Engineering teams actually use it for.

01Privacy-safe training datasets with statistical distribution preservedGenerate synthetic datasets that match the statistical distribution, correlation structure, and null rates of production, without any real PII. FK-preserving extraction maintains referential integrity. Your models train on data that behaves like production.

02Differential privacy mode for GDPR and HIPAA compliant synthetic outputsDifferential privacy noise injection with configurable epsilon. GDPR data minimisation and HIPAA safe harbour compliant synthetic outputs. Quantifiable privacy guarantee on every dataset.

03Direct Parquet and S3 export to training pipelinesExport directly to Parquet, S3, Delta Lake, or your existing data lake. No intermediate CSV steps. Compatible with PyTorch, TensorFlow, and HuggingFace data loading patterns.

04Scheduled production-to-training data refreshConfigure once. The production-to-training pipeline runs automatically. ML team always has a current, privacy-safe dataset without accessing production systems or raising a ticket.

Platform capabilities

How VestraData maps to this environment.

FK-preserving extraction

Referential integrity preserved across related tables. Relational structure of production data maintained in synthetic output.

Differential privacy (configurable ε)

Noise injection with configurable epsilon. Quantifiable privacy guarantees on every synthetic dataset, not just a qualitative claim.

Parquet and S3 export

Direct export to Parquet, S3, and Delta Lake. Column names and schema preserved. Works with PyTorch, TensorFlow, and HuggingFace DataLoaders.

Scheduled refresh pipeline

Configure once. Refresh runs automatically on your schedule. ML team always has a current dataset. No access to production required.

Distribution matching

Statistical distribution, correlation structure, and null rates matched to production. Edge cases preserved in synthetic output.

GDPR erasure compliance

Synthetic training data means erasure requests don't create retraining obligations. The personal data was never in the training set.

Companion tool · VestraShield

Your data scientists query production systems with AI assistance every day.

VestraShield intercepts every AI-assisted notebook execution, API call, and IDE completion. Customer records, patient identifiers, financial data: none of it reaches the LLM without being governed first.

Notebook LLM interceptEvery AI-assisted cell execution in Jupyter, Colab, or similar environments is intercepted. Production-like data in notebooks doesn't reach external LLMs.
IDE completion interceptGitHub Copilot, Cursor, and code assistant completions governed. Data scientists writing training code are covered without changing their workflow.
Production query governanceAI-assisted queries against production-like data stores intercepted at the HTTP proxy layer. No application changes required.
Per-team policy engineData science, ML engineering, and analytics teams get different intercept rules. Fine-grained configuration without a separate deployment per team.