# Autopilot model deployment and prediction
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This Amazon SageMaker Autopilot guide includes steps for model deployment, setting up real-time inference, and running inference with batch jobs. 

After you train your Autopilot models, you can deploy them to get predictions in one of two ways:

1. Use [Deploy models for real-time inference](autopilot-deploy-models-realtime.md) to set up an endpoint and obtain predictions interactively. Real-time inference is ideal for inference workloads where you have real-time, interactive, low latency requirements.

1. Use [Run batch inference jobs](autopilot-deploy-models-batch.md) to make predictions in parallel on batches of observations on an entire dataset. Batch inference is a good option for large datasets or if you don't need an immediate response to a model prediction request.

**Note**  
To avoid incurring unnecessary charges: After the endpoints and resources that were created from model deployment are no longer needed, you can delete them. For information about pricing of instances by Region, see [Amazon SageMaker Pricing](https://www.amazonaws.cn/sagemaker/pricing/).