Deploying and monitoring multiple models is significantly complex, so we depend on TorchServe, SageMaker’s default PyTorch model serving library. Jointly developed by Facebook’s PyTorch team and AWS to streamline the transition from prototyping to production, TorchServe helps us deploy trained PyTorch models at scale without having to write custom code. It provides a secure set of REST APIs for inference, management, metrics, and explanations. With features such as multi-model serving, model versioning, ensemble support, and automatic batching, TorchServe is ideal for supporting our immense workload. You can read more about deploying your Pytorch models on SageMaker with native TorchServe integration in this blog post.