Serverless

Learn how to deploy a Hugging Face Gradio Application using Hugging Face Transformers to AWS Lambda for serverless workloads.

Learn how to deploy a Transformer model like BERT to Amazon SageMaker Serverless using the Python SageMaker SDK.

Learn how to deploy Hugging Face Transformers serverless using the new Amazon SageMaker Serverless Inference.

Learn how to build a Multilingual Serverless BERT Question Answering API with a model size of more than 2GB and then testing it in German and France.

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.

Learn how to build and deploy an AWS Lambda function with a custom python docker container as runtime with the use of Amazon ECR.

Build a serverless Question-Answering API using the Serverless Framework, AWS Lambda, AWS EFS, efsync, Terraform, the transformers Library from HuggingFace, and a `mobileBert` model from Google fine-tuned on SQuADv2.

efsync is a CLI/SDK tool, which syncs files from S3 or local filesystem automatically to AWS EFS and enables you to install dependencies with the AWS Lambda runtime directly into your EFS filesystem.

Leverage your Serverless architectures with mounting your AWS EFS volume into your AWS Lambda with the Serverless Framework.

Build a serverless question-answering API with BERT, HuggingFace, the Serverless Framework and AWS Lambda.

Scale your machine learning models by using AWS Lambda, the Serverless Framework, and PyTorch. I will show you how to build scalable deep learning inference architectures.