Model deployment options in Amazon SageMaker AI - Amazon SageMaker AI

Model deployment options in Amazon SageMaker AI

After you train your machine learning model, you can deploy it using Amazon SageMaker AI to get predictions. Amazon SageMaker AI supports the following ways to deploy a model, depending on your use case:

SageMaker AI also provides features to manage resources and optimize inference performance when deploying machine learning models:

  • To manage models on edge devices so that you can optimize, secure, monitor, and maintain machine learning models on fleets of edge devices, see Model deployment at the edge with SageMaker Edge Manager. This applies to edge devices like smart cameras, robots, personal computers, and mobile devices.

  • To optimize Gluon, Keras, MXNet, PyTorch, TensorFlow, TensorFlow-Lite, and ONNX models for inference on Android, Linux, and Windows machines based on processors from Ambarella, ARM, Intel, Nvidia, NXP, Qualcomm, Texas Instruments, and Xilinx, see Model performance optimization with SageMaker Neo.

For more information about all deployment options, see Deploy models for inference.