# Real-time forecasting
<a name="timeseries-forecasting-realtime"></a>

Real-time forecasting is useful when you need to generate predictions on-the-fly, such as for applications that require immediate responses or when forecasting for individual data points.

By deploying your AutoML model as a real-time endpoint, you can generate forecasts on-demand and minimize the latency between receiving new data and obtaining predictions. This makes real-time forecasting well-suited for applications that require immediate, personalized, or event-driven forecasting capabilities.

For real time forecasting, the dataset should be a subset of the input dataset. The real time endpoint has an input data size of approximately 6MB and a response timeout limitation of 60 seconds. We recommend bringing in one or few items at a time.

You can use SageMaker APIs to retrieve the best candidate of an AutoML job and then create a SageMaker AI endpoint using that candidate.

Alternatively, you can chose the automatic deployment option when creating your Autopilot experiment. For information on setting up the automatic deployment of models, see [How to enable automatic deployment](autopilot-create-experiment-timeseries-forecasting.md#timeseries-forecasting-auto-model-deployment).

**To create a SageMaker AI endpoint using your best model candidate:**

1. 

**Retrieve the details of the AutoML job.**

   The following AWS CLI command example uses the [DescribeAutoMLJobV2](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_DescribeAutoMLJobV2.html) API to obtain details of the AutoML job, including the information about the best model candidate.

   ```
   aws sagemaker describe-auto-ml-job-v2 --auto-ml-job-name job-name --region region
   ```

1. 

**Extract the container definition from [InferenceContainers](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_AutoMLCandidate.html#sagemaker-Type-AutoMLCandidate-InferenceContainers) for the best model candidate.**

   A container definition is the containerized environment used to host the trained SageMaker AI model for making predictions.

   ```
   BEST_CANDIDATE=$(aws sagemaker describe-auto-ml-job-v2 \
     --auto-ml-job-name job-name 
     --region region \
     --query 'BestCandidate.InferenceContainers[0]' \
     --output json
   ```

   This command extracts the container definition for the best model candidate and stores it in the `BEST_CANDIDATE` variable.

1. 

**Create a SageMaker AI model using the best candidate container definition.**

   Use the container definitions from the previous steps to create a SageMaker AI model by using the [CreateModel](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateModel.html) API.

   ```
   aws sagemaker create-model \
               --model-name 'your-candidate-name>' \
               --primary-container "$BEST_CANDIDATE"
               --execution-role-arn 'execution-role-arn>' \
               --region 'region>
   ```

   The `--execution-role-arn` parameter specifies the IAM role that SageMaker AI assumes when using the model for inference. For details on the permissions required for this role, see [CreateModel API: Execution Role Permissions](https://docs.aws.amazon.com/).

1. 

**Create a SageMaker AI endpoint configuration using the model.**

   The following AWS CLI command uses the [CreateEndpointConfig](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateEndpointConfig.html) API to create an endpoint configuration.

   ```
   aws sagemaker create-endpoint-config \
     --production-variants file://production-variants.json \
     --region 'region'
   ```

   Where the `production-variants.json` file contains the model configuration, including the model name and instance type.
**Note**  
We recommend using [m5.12xlarge](https://aws.amazon.com/ec2/instance-types/m5/) instances for real-time forecasting.

   ```
   [
       {
         "VariantName": "variant-name",
         "ModelName": "model-name",
         "InitialInstanceCount": 1,
         "InstanceType": "m5.12xlarge"
       }
     ]
   }
   ```

1. 

**Create the SageMaker AI endpoint using the endpoint configuration.**

   The following AWS CLI example uses the [CreateEndpoint](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateEndpoint.html) API to create the endpoint.

   ```
   aws sagemaker create-endpoint \
               --endpoint-name 'endpoint-name>' \
               --endpoint-config-name 'endpoint-config-name' \
               --region 'region'
   ```

   Check the progress of your real-time inference endpoint deployment by using the [DescribeEndpoint](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_DescribeEndpoint.html) API. See the following AWS CLI command as an example.

   ```
   aws sagemaker describe-endpoint \
               --endpoint-name 'endpoint-name' \
               --region 'region'
   ```

   After the `EndpointStatus` changes to `InService`, the endpoint is ready to use for real-time inference.

1. 

**Invoke the SageMaker AI endpoint to make predictions.**

   ```
   aws sagemaker invoke-endpoint \
               --endpoint-name 'endpoint-name' \ 
               --region 'region' \
               --body file://input-data-in-bytes.json \
               --content-type 'application/json' outfile
   ```

   Where the `input-data-in-bytes.json` file contains the input data for the prediction.