Deploying high-quality, trained machine learning (ML) models to perform either batch or real-time inference is a critical piece of bringing value to customers. However, the ML experimentation process can be tedious—there are a lot of approaches requiring a significant amount of time to implement. That’s why pre-trained ML models like the ones provided in the PyTorch Model Zoo are so helpful. Amazon SageMaker provides a unified interface to experiment with different ML models, and the PyTorch Model Zoo allows us to easily swap our models in a standardized manner.
This blog post demonstrates how to perform ML inference using an object detection model from the PyTorch Model Zoo within SageMaker. Pre-trained ML models from the PyTorch Model Zoo are ready-made and can easily be used as part of ML applications. Setting up these ML models as a SageMaker endpoint or SageMaker Batch Transform job for online or offline inference is easy with the

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