End-to-end (E2E) artificial intelligence (AI) pipelines are composed of
several stages including data preprocessing, data ingestion, defining and
training the model, hyperparameter optimization, deployment, inference,
postprocessing, followed by downstream analyses. To obtain efficient E2E
workflow, it is required to optimize almost all the stages of pipeline. Intel
Xeon processors come with large memory capacities, bundled with AI acceleration
(e.g., Intel Deep Learning Boost), well suited to run multiple instances of
training and inference pipelines in parallel and has low total cost of
ownership (TCO). To showcase the performance on Xeon processors, we applied
comprehensive optimization strategies coupled with software and hardware
acceleration on variety of E2E pipelines in the areas of Computer Vision, NLP,
Recommendation systems, etc. We were able to achieve a performance improvement,
ranging from 1.8x to 81.7x across different E2E pipelines. In this paper, we
will be highlighting the optimization strategies adopted by us to achieve this
performance on Intel Xeon processors with a set of eight different E2E
pipelines.

本文研究了在 Intel Xeon 处理器上，优化终端到终端的人工智能管道，通过多种优化策略和软硬件加速在计算机视觉、NLP、推荐系统等多个领域展示了高性能，并获得了 1.8 倍至 81.7 倍的性能提升。