Domino Data Science Blog

In the last few years, machine learning and deep learning have become a core part of fields like healthcare, retail, banking, and insurance. This list can go on indefinitely—there's almost no field where machine learning (ML) is not applied to improve the overall efficiency and accuracy of systems. As the world's economy has rapidly grown, so has the need to do things automatically.

It’s now normal for computational power to be improved continuously over time. Monthly, or at times even weekly, new devices are being created with better features and increased processing power. However, these enhancements require intensive hardware resources. The question then is, Are you able to use all the computational resources that a device provides? In most cases, the answer is no, and you’ll get an out of memory error. But how can you make use of all the computational resources without changing the underlying architecture of your project?