Learn how to automatically save your model weights, logs, and artifacts to the Hugging Face Hub using Amazon SageMaker and how to deploy the model afterwards for inference.
If you are like me you are not from the USA and cannot easily travel to Las Vegas. I have the perfect remote guide for your perfect virtual re:Invent 2021 focused on NLP and Machine Learning.
Learn how to build an End-to-End MLOps Pipeline for Hugging Face Transformers from training to production using Amazon SageMaker.
Learn how to add auto-scaling to your Hugging Face Transformers SageMaker Endpoints.
🌸 BigScience released their first modeling paper introducing T0 which outperforms GPT-3 on many zero-shot tasks while being 16x smaller! Deploy BigScience the 3 Billion version (T0_3B) to Amazon SageMaker with a few lines of code to run a scalable production workload!
Deploy Hugging Face Transformers to Amazon SageMaker and create an API for the Endpoint using AWS Lambda, API Gateway and AWS CDK.
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.