Subject archive for "perspective," page 5

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. The data science industry will need to embrace parallelization and GPU processing to efficiently utilize increasingly complex datasets.

Algorithms may be the toast of today’s high-performance technology races, but sometimes proponents forget that, like cars, models also need a regular tune-up. A highly visible and catastrophic AI model failure recently shamed Zillow, the online real estate company that was forced to shutter its home-buying business.

Editor’s note: From May 14 - 21, 2021, nearly 100 data scientists across Latin America joined BNP Paribas Cardif’s Virtual Data Science Hackathon. Here at Domino, we were excited to have been a part of this event, supporting both the next generation of data scientists and the next generation of insurance solutions.

Editor’s note: This is part of a series of articles sharing best practices from companies developing an enterprise data science strategy. Some articles will include information about their use of Domino.

Doing data science productively is akin to the work of a master craftsman: efficiently producing a quality job is wholly dependent on having a unique set of tools, and a workbench that allows the skilled, systematic use of those tools for a custom finished product.