This deep dive explores the intersection of two trends in machine learning pipelines: state-of-the-art models in natural language processing and serverless cloud architectures. Since Google published Word2Vec in 2013, word embeddings became rapidly more powerful. However, this went along with a substantial increase in computational complexity. On the other hand, the shift to serverless architectures emphasizes lean modules, which are not suited for heavy calculations. In this deep dive, Timo will walk you through an implementation of a pretrained model into a serverless infrastructure on AWS. Afterward, you will have a blueprint that is easy to adapt to your use cases.