# Model deployment options in Amazon SageMaker AI
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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:
+ For persistent, real-time endpoints that make one prediction at a time, use SageMaker AI real-time hosting services. See [Real-time inference](realtime-endpoints.md).
+ Workloads that have idle periods between traffic spikes and can tolerate cold starts, use Serverless Inference. See [Deploy models with Amazon SageMaker Serverless Inference](serverless-endpoints.md).
+ Requests with large payload sizes up to 1GB, long processing times, and near real-time latency requirements, use Amazon SageMaker Asynchronous Inference. See [Asynchronous inference](async-inference.md).
+ To get predictions for an entire dataset, use SageMaker AI batch transform. See [Batch transform for inference with Amazon SageMaker AI](batch-transform.md).

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](edge.md). 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](neo.md).

For more information about all deployment options, see [Deploy models for inference](deploy-model.md).