About Synthetic Data

Synthetic data are artificially created data sets that imitate real data but do not directly contain confidential or personal information. They are generated with the help of special algorithms and models that can reproduce various characteristics and structures of real data.

Synthetic data play a key role in the artificial intelligence training, as they allow developers to explore and test algorithms and models on large volumes of data without risking breaches of confidentiality or accessing real data. They are also used for expanding training data sets, improving the quality of models and algorithms, and creating diverse scenarios and test cases.

The advantages of synthetic data include flexibility in the creation process, the lack of dependence on the accessibility for real data, and the ability to generate data for various scenarios and cases of use. They also help to improve data privacy protection, as they do not require direct access to real personal data.

In the field of artificial intelligence and machine learning, synthetic data are used for training and testing models, analysing data, creating predictive models, and decision-making. They are used in various areas such as medicine, finance, transportation, biotechnology, cybersecurity, and many others, where the analysis of large volumes of data and the training of artificial intelligence models are required.