# About the project

One of the main problems faced by machine learning specialists is the limited access to real data. This can be caused by privacy constraints, data unavailability or limited volume.

However, training models and algorithms becomes challenging without a sufficient volume and variety of data. That leads to significant slowdown in progress with artificial intelligence development and application.

Our project offers an innovative approach to solving the data access problem by providing a tool for generating synthetic data. We use advanced data generation methods based on machine learning and deep learning technologies, allowing us to create data that is highly realistic and suitable for training various types of models.

Our innovative approach involves combining various data generation methods and developing algorithms to assess the quality of synthetic data. We offer users the ability to generate synthetic data according to their specific request, taking into account their needs and data requirements. Users can customise data generation parameters such as format, volume, structure, and distribution to obtain perfectly suitable data for their machine learning projects.

We aim to provide machine learning professionals with a tool that not only provides access to data but also ensures high quality of their synthesis, promoting further progress in the field of artificial intelligence.

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