Deployment on SageMaker is straightforward because it uses the SageMaker Hugging Face Inference Toolkit, an open-source library for serving Transformers models on SageMaker. We normally don't even have to provide an inference script; the toolkit takes care of that. In that case, however, the toolkit utilizes the Pipeline API again, and as we discussed in section 2, the Pipeline API doesn't allow us to use advanced text generation techniques such as beam search and sampling. To avoid this limitation, we provide our custom inference script. For the first evaluation of our newly trained model, we use the same parameters as in section 2 with the zero-shot model to generate the candidate summaries.

When OpenAI released the third generation of their machine learning (ML) model that specializes in text generation in July 2020, I knew something was different. This model struck a nerve like no one that came before it. Suddenly I heard friends and colleagues, who might be interested in technology but usually don't care much about the latest advancements in the AI/ML space, talk about it. Even the Guardian wrote an article about it. Or, to be precise, the model wrote the article and the Guardian edited and published it. There was no denying it – GPT-3 was a game changer.