The last few years have seen rapid growth in the field of natural language processing (NLP) using transformer deep learning architectures. With its Transformers open-source library and machine learning (ML) platform, Hugging Face makes transfer learning and the latest transformer models accessible to the global AI community. This can reduce the time needed for data scientists and ML engineers in companies around the world to take advantage of every new scientific advancement. Amazon SageMaker and Hugging Face have been collaborating to simplify and accelerate adoption of transformer models with Hugging Face DLCs, integration with SageMaker Training Compiler, and SageMaker distributed libraries.
SageMaker provides different options for ML practitioners to deploy trained transformer models for generating inferences:

Real-time inference endpoints, which are suitable for workloads that need to be processed with low latency requirements in the order of milliseconds.
Batch transform, which is ideal for offline predictions on

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