Information that is produced artificially rather than by actual events is known as synthetic data. Synthetic data is used to test mathematical models and train machine learning models. It is often produced using algorithms.
Importance of synthetic data
To train neural networks, developers require vast, meticulously annotated datasets. AI models are typically more accurate when they have more varied training data. The issue is that compiling and identifying datasets that could include a few thousand to tens of millions of elements takes much effort and is frequently unaffordable. So it is convenient to use synthetically produced data. Synthetic data also proves to be an essential asset when data is unavailable due to security concerns or when the data isn’t sufficient to train the model.
Tools for creating synthetic data can provide quick and easy ways to replicate sensitive and priceless data assets, such as patient journeys in healthcare or transaction data in banking. Without the weight of red tape, threats to privacy, and loss of data utility, these synthetic consumer datasets can be shared and collaboratively worked on securely. Sharing, modifying, discarding, adjustable sizing, and enhancing synthetic data sets are necessary for the construction of AI and machine learning models and for the governance, explainability, and governance of AI/ML models. The advantages of synthetic data are that they are cheap, maintain privacy, and can be produced quickly.
Some of the synthetic data generation tools
Types of synthetic data
Synthetic data can be of different types, like synthetic text data, synthetic tabular data, synthetic videos, images, and sound.
Synthetic data stored in tables but generated artificially is referred to as tabular synthetic data. There are columns and rows of data here. It may be anything from a patient database to information about users’ analytical behavior or financial logs.
A synthetic video, image, or sound can also be a manufactured piece of data. You create media that has characteristics that are reasonably close to data from the real world. Due to their similarities, fake media can easily take the place of actual data. This technique will help the databases used to train machine learning algorithms. For instance, you can create synthetic video data to replace training data if it is unavailable due to privacy concerns. The number and diversity of datasets can also be increased using synthetic data while training image recognition algorithms.
Artificially produced text is known as synthetic text. You create and hone a model to produce text. It has always been challenging to develop accurate synthetic writing because of the complexity of languages. However, the development of highly effective natural language production systems was made possible by the introduction of new machine learning models.
Niharika is a Technical consulting intern at Marktechpost. She is a third year undergraduate, currently pursuing her B.Tech from Indian Institute of Technology(IIT), Kharagpur. She is a highly enthusiastic individual with a keen interest in Machine learning, Data science and AI and an avid reader of the latest developments in these fields.