The latest developments in NLP show that you can overcome this limitation by providing a few examples at inference time with a large language model - a technique known as Few-Shot Learning. In this blog post, we'll explain what Few-Shot Learning is, and explore how a large language model called GPT-Neo.
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.
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.
This is the Story of how I became a certified solution architect within 28 hours of preparation.