Machine Learning time-to-train reduced significantly with scaled clusters connected with Intel® OPA.
The ever-increasing volume, velocity and variety of data is creating both challenges and opportunities in every industry as they increasingly deploy AI/Machine Learning for their digital transformation. Training a model with larger datasets typically produces more inferencing accuracy but... this could take weeks/days. Parallel HPC-like systems coupled with productivity-enhancing software tools and libraries are increasingly being used to scale ML efficiently, particularly as faster Training and re-training on new data become key requirements to improve Inference accuracy.
Intel provides a comprehensive AI/ML solutions portfolio with optimized frameworks, development tools, libraries, multiple high-performance processors and the Intel Omni-Path Architecture (Intel® OPA). Intel’s pioneering parallel computing research in AI/ML and unique Intel® OPA features reduce communications overheads and enhance computational efficiency at scale. In fact, recently, a large scale cluster system with Intel® processors connected with the Intel® OPA fabric broke several records for image recognition and other ML workloads.