Use deep learning frameworks natively in Amazon SageMaker Processing

Until recently, customers who wanted to use a deep learning (DL) framework with Amazon SageMaker Processing faced increased complexity compared to those using scikit-learn or Apache Spark. This post shows you how SageMaker Processing has simplified running machine learning (ML) preprocessing and postprocessing tasks with popular frameworks such as PyTorch, TensorFlow, Hugging Face, MXNet, and XGBoost. Training an ML model takes many steps. One of them, data preparation, is paramount to creating an accurate ML model. Likewise, you often need to run postprocessing jobs (for example, filtering or collating) and model evaluation jobs (scoring models against different test sets) as part of your ML model development lifecycle.