Gretel goes GA with privacy engineering developer stack

If you don’t know what comprises synthetic data, well, don’t worry; you have plenty of company. Synthetic data is information that’s artificially manufactured by machines rather than generated by real-world events. Synthetic data is created algorithmically and is used as a stand-in for test datasets of production or operational data to validate mathematical models and, increasingly, to train machine-learning models. This substitutional data helps preserve privacy in personal information and can save IT systems a great deal of time, trouble, and money in the process.

When machine-learning models are being created, the data has to be pure; if there are errors, duplications, or other hiccups in real data in building such models, problems inevitably will surface, costing time and money for the company. With more and more artificial intelligence and machine-learning models being used in various use cases, the need for synthetic data is rapidly growing. Analysts have projected that more synthetic than original data will be used to build ML models by the end of the decade.

There are companies focusing on the commercial business use of synthetic data, and one of the first is Gretel, based in San Diego, Calif. The 2-year-old startup on Feb. 1 announced the general availability

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