Scale Vision Transformers (ViT) Beyond Hugging Face 1/3

I am one of the contributors to the Spark NLP open-source project and just recently this library started supporting end-to-end Vision Transformers (ViT) models. I use Spark NLP and other ML/DL open-source libraries for work daily and I have decided to deploy a ViT pipeline for a state-of-the-art image classification task and provide in-depth comparisons between Hugging Face and Spark NLP. The purpose of this article is to demonstrate how to scale out Vision Transformer (ViT) models from Hugging Face and deploy them in production-ready environments for accelerated and high-performance inference. By the end, we will scale a ViT model from Hugging Face by 25x times (2300%) by using Databricks, Nvidia, and Spark NLP. Back in 2017, a group of researchers at Google AI published a paper that introduced a transformer model architecture that changed all Natural Language Processing (NLP) standards.