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The traditional paradigm for provisioning data for software testing is evolving. What the industry currently refers to as Test Data Management (TDM) is changing with the times. Everything associated with the software release pipeline is being automated and integrated, except, that is, for the traditional and monolithic TDM model. With the help of synthetic data and Test Data Automation (TDA), software development and testing teams can unlock new levels of quality and efficiency.

Some analysts believe that the market for machine learning may surpass $9 Billon USD this year. The surge in growth in the ML market is exponential as new avenues for software development and applications emerge. With this surge in new applications comes the need for massive volumes of data to train ML models to perform at a high level of accuracy and consistency. Here, we present an overview of the role synthetic data can play in training machine learning algorithms. And we’ll identify the best applications for GenRocket’s Synthetic Test Data Automation platform in this rapidly growing industry.

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