Interesting Engineering | Significance of Synthetic Data in Machine Learning Engineering
Synthetic data is a necessity, not just an option.
Not everything that is inorganic, manufactured or synthetic is fake or inferior. This assertion is particularly true when it comes to synthetic data in the context of machine learning. Simulated data is not only useful but also more practical when compared to real or actual data, in some cases.
In the field of machine learning, synthetic data is crucial to ensure that an AI system has been trained sufficiently before it is deployed. Machine learning engineering, the process of producing a machine learning (ML) model with the help of software engineering and data science principles, will encounter critical difficulties without synthetic data.