Amazon SageMaker
Amazon SageMaker is a cloud-based machine learning service from Amazon used to build and deploy predictive analytics solutions.
Integrations and Connectors
AgilePoint NX integrates in these ways with Amazon SageMaker:
- AI Control Towers
- Lookups in eForm Builder
- Access tokens
AI Control Towers
You can use Amazon SageMaker AI Control Towers to orchestrate operations within your apps.
An AI Control Tower is a process activity used for robust, secure monitoring that work with predictive AI to improve workflows with dynamic, predictive capabilities. These predictive AI agents enable real-time monitoring, control, and adaptation for process instances, dynamically adjusting them based on AI-driven predictions. AI Control Towers seamlessly handle AI agent integration and orchestration across multiple apps. AI Control Towers also provides centralized, real-time monitoring and analytics features that leverage AI to help organizations oversee their operations, improve governance, and optimize their business processes.

The following AI Control Towers are available for Amazon SageMaker:
- Get Image Classification
- Get Prediction
- Image Classification - Adapt Flow
- Image Classification - Initiate Subprocess
- Image Classification - Send Notification
- Prediction - Adapt Flow
- Prediction - Initiate Subprocess
- Prediction - Send Notification
For more information, refer to the AI Control Towers for Amazon SageMaker in Process Builder.
Lookups in eForm Builder
You can create lookups for Amazon SageMaker in eForm Builder and form-based apps.
The lookup can extract information from large unstructured datasets in a SageMaker model, such as free-text fields, and give consistent responses across multiple apps.
A lookup is an automated procedure that retrieves data from an external data source, such as a database or third-party service, to display runtime. For example, you can use a lookup to populate the options in a list on an eForm.
For more information, refer to Amazon SageMaker lookup configuration.


This image is only one example configuration screen from the Auto-Lookup form control. It does not show all the configuration options, and lookups can be configured and executed in multiple ways. The Auto-Lookup control can perform a lookup for eForms or other controls. However, many other form controls have built-in lookup capabilities — for example, the Drop-Down List form control. For information about how to configure lookups, refer to eForm Controls.
Access Tokens
You can create access tokens for Amazon Web Services.
An access token is a secure object that stores an endpoint (usually a URL) and authentication credentials to connect to a service or technology. Often this is an external or third-party service, like Salesforce or SharePoint, but access tokens can also connect to an AgilePoint NX enviornment, local database, or other types of technologies. Once an access token is created, app designers can simply select and reuse it, rather than entering the credentials each time they are needed.
For more information, refer to Access tokens for Amazon Web Services.

Related Topics
- Amazon SageMaker tab - Process activities
- Access tokens for Amazon Web Services
Examples
- (External) Supercharge Your end-to-end Orchestration with AI Control Tower: Real-Time Monitoring and Smart Decision Making
- (External) AI Control Tower – Predictive AI Agents for Image Classification
- Examples - Step-by-step use case examples, information about what types of examples are provided in the AgilePoint NX Product Documentation, and other resources where you can find more examples.
Amazon SageMaker Documentation
Use these links to find third-party vendor documentation for Amazon SageMaker:
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Keywords
Amazon SageMaker, sage maker, Amazon Web Services, aws, machine learning, AI, artificial intelligence