GenRocket Blog

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.

Software performance testing is a form of non-functional testing that’s critical to providing a great user experience and ensuring the stability and scalability of any software application under heavy load conditions. To fully exercise your code, you’ll need a high volume of test data to simulate the most demanding real-world operating conditions.

The GenRocket Test Data Automation platform has been in development for more than 10 years with a vision of accelerating and automating the delivery of accurate, controlled test data into any testing environment.

The healthcare sector is on the verge of a revolution in data and analytics, but the advancement of data-driven decision making has been hampered by difficulties in updating legacy systems, as well as challenges stemming from disparate data sources. With the growing push to digitize patient and claims information, the evolution of healthcare data exchange standards has come a long way to address some of these problems, but not all of them.

Enterprise Metadata Management is technology used to centrally manage and deliver high quality data and trusted information for business analysis and decision-making. Metadata is often referred to as “data about data” and describes the content, governance, and structure of enterprise information. Metadata is often used to create data catalogs that aggregate, group, and sort multiple data sources to make them accessible for a wide variety of use cases.