Learn how to train distributed models for summarization using Hugging Face Transformers and Amazon SageMaker and upload them afterwards to huggingface.co.
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.
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.
Fine-tune non-English, German GPT-2 model with Huggingface on German recipes. Using their Trainer class and Pipeline objects.
Build a serverless question-answering API with BERT, HuggingFace, the Serverless Framework and AWS Lambda.
Build a non-English (German) BERT multi-class text classification model with HuggingFace and Simple Transformers.
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.
