What is synthetic data?

Definition: Synthetic data is artificially generated data that mimics the patterns and structure of real data, used to train, test or evaluate software and AI models when real data is scarce, sensitive or hard to label.

How synthetic data is made

Methods range from rule-based generators and simulations to statistical models and generative AI. A language model might write varied customer questions for an evaluation set; a simulator might produce sensor readings; a tabular generator might create records that preserve the statistics of a real dataset without copying individuals.

Why it matters

Synthetic data can fill gaps for rare cases, protect privacy in test environments, speed up labeling and stress-test systems with edge cases. It is widely used for fine-tuning examples and for evaluation sets.

Limits and risks

Generated data inherits the assumptions of whatever produced it. If it does not reflect real inputs, models can look good in testing and fail in production. Teams validate synthetic sets against real samples, keep a real-data holdout for final evaluation and check that generated records cannot be traced back to real people.

How Ryz helps

Ryz data scientists and machine learning engineers build and validate synthetic datasets for training and evaluation, working on your team.

Related

• Machine learning development
• LLM evaluation services
• LLM fine-tuning
• Data pipeline