---
title: "Predict Using Model"
slug: "predict-using-model"
updated: 2025-08-14T15:28:53Z
published: 2025-08-14T15:28:53Z
canonical: "documentation.decisions.com/predict-using-model"
---

> ## Documentation Index
> Fetch the complete documentation index at: https://documentation.decisions.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Predict Using Model

| Step Details |
| --- |
| **Introduced in Version** | 9.0.0 |
| **Last Modified in Version** | 9.0.0 |
| **Location** | Integration > Amazon AWS > Machine Learning |

The **Predict Using Model** step is used to use a specified model to predict an outcome based on how the specified model was trained.

### Prerequisites

- This step requires the [Amazon AWS Module](https://documentation.decisions.com/docs/aws) to be [installed](https://documentation.decisions.com/docs/installing-modules-decisions) before it will be available in the toolbox, and an active [AWS account](https://signin.aws.amazon.com/signin?redirect_uri=https%3A%2F%2Fconsole.aws.amazon.com%2Fconsole%2Fhome%3FhashArgs%3D%2523%26isauthcode%3Dtrue%26nc2%3Dh_ct%26src%3Dheader-signin%26state%3DhashArgsFromTB_eu-north-1_c1412528899e4833&client_id=arn%3Aaws%3Asignin%3A%3A%3Aconsole%2Fcanvas&forceMobileApp=0&code_challenge=HQku5BPrRUboP5p1wGr4Ai_nBIQNSqsR2eHJNCBfxhQ&code_challenge_method=SHA-256). The [Amazon WebServices Settings](https://documentation.decisions.com/docs/setting-up-amazon-webservices-integration) must also be configured.
- A dependency must be created after installing the module in order for the the steps to be properly used.

AWS Machine Learning is only available to preexisting customers that previously had access to this process. If access to the Machine Learning process is unavailable, consider using [Amazon SageMaker](https://documentation.decisions.com/docs/aws-sagemaker-steps) as an alternative.

---

## Properties

#### Inputs

| Property | Description | Data Type |
| --- | --- | --- |
| Model Data | List of data constructed in correlation with the model for predicting. | List of MachineLearningModelData |
| Model Id | Id of the model to use for predicting. | String |
| Predict Endpoint | Intended point for the prediction to be calculated to. | String |
| Region | The Amazon AWS Region the model is located in. | Amazon AWSRegion |

#### Outputs

| Property | Description | Data Type |
| --- | --- | --- |
| Done |  |  |
|  | Output | Outputted complete prediction made from the inputted information. | String |

![](https://cdn.document360.io/6ef8bcc1-6489-4486-9ad1-83acff7e5df0/Images/Documentation/2025-08-13_10h47_28.png)

---

## Common Errors

### The Security Token Included in the Request is Invalid

If the token being used is expired or does not exist, the step will cause an error. To correct this, input an active Machine Learning token.

#### Exception Message:

```csharp
DecisionsFramework.Design.Flow.ErrorRunningFlowStep: Error running step Delete Model 1[DeleteModel] in flow [Machine Learning Steps Flow]: Exception invoking method DeleteModel on class MachineLearningSteps
 ---> DecisionsFramework.LoggedException: Exception invoking method DeleteModel on class MachineLearningSteps
 ---> Amazon.MachineLearning.AmazonMachineLearningException: The security token included in the request is invalid.
 ---> Amazon.Runtime.Internal.HttpErrorResponseException: Exception of type 'Amazon.Runtime.Internal.HttpErrorResponseException' was thrown. at Amazon.Runtime.HttpWebRequestMessage.ProcessHttpResponseMessage(HttpResponseMessage responseMessage)
 at Amazon.Runtime.HttpWebRequestMessage.GetResponseAsync(CancellationToken cancellationToken)
 at Amazon.Runtime.Internal.HttpHandler`1.InvokeAsync[T](IExecutionContext executionContext)
 at Amazon.Runtime.Internal.Unmarshaller.InvokeAsync[T](IExecutionContext executionContext)
 at Amazon.Runtime.Internal.ErrorHandler.InvokeAsync[T](IExecutionContext executionContext)
   --- End of inner exception stack trace ---
```

---

Related Information

Related Article:

[AWS SageMaker](https://documentation.decisions.com/docs/aws-sagemaker-steps)