Serverless BERT with HuggingFace, AWS Lambda, and Docker

Serverless BERT with HuggingFace, AWS Lambda, and Docker

Serverless BERT with HuggingFace, AWS Lambda, and Docker. Andy Jassy presented the AWS Lambda Container Support, which allows us to use custom container (docker). The benefits of serverless architectures to leverage Google’s “State-of-the-Art” NLP Model.

Learn how to use the newest cutting edge computing power of AWS with the benefits of serverless architectures to leverage Google’s “State-of-the-Art” NLP Model

Introduction

It’s the most wonderful time of the year. Of course, I’m not talking about Christmas but re:Invent. It is re:Invent time.

In the opening keynote, Andy Jassy presented the AWS Lambda Container Support, which allows us to use custom container (docker) images up to 10GB as a runtime for AWS Lambda. With that, we can build runtimes larger than the previous 250 MB limit, be it for “State-of-the-Art” NLP APIs with BERT or complex processing.

Furthermore, you can now configure AWS Lambda functions with up to  10 GB of Memory and 6 vCPUs.

For those who are not that familiar with BERT was published in 2018 by Google and stands for Bidirectional Encoder Representations from Transformers and is designed to learn word representations or embeddings from an unlabeled text by jointly conditioning on both left and right context. Transformers are since that the “State-of-the-Art” Architecture in NLP.

created by the author,  Philipp Schmid

Google Search started using BERT end of 2019 in  1 out of 10 English searches, since then the usage of BERT in Google Search increased to almost  100% of English-based queries. But that’s not it. Google powers now over  70 languages with BERT for Google Search.

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