Machine learning’s impact on technology is significant, but it’s crucial to acknowledge the common issues of insufficient training and testing data.

Where real data is unethical, unavailable, or doesn’t exist, synthetic data sets can provide the needed quantity and variety.

Machine learning models are trained with huge amounts of data and must be tested before practical use. For this, the data must first be divided into a larger training set and a smaller test set ...

Better testing means better software. Using NLP, test data generation, and optimized testing can quickly improve applications.