Increasing model velocity for complex models by leveraging hybrid pipelines, parallelization and…

Increasing model velocity for complex models by leveraging hybrid pipelines, parallelization and GPU acceleration

How to choose the right tools to tackle complex models

Data science is facing an overwhelming demand for CPU cycles as scientists try to work with datasets that are growing in complexity faster than Moore’s Law can keep up. Considering the need to iterate and retrain quickly, model complexity has been outpacing available compute resources and CPUs for several years, and the problem is growing quickly. …

data science editors pick gpu-acceleration hybrid machine learning moores-law pipelines