Resources
Users must have the COLEMANAI-User or the COLEMANAI-Administrator role to assign resources to tags.
This table describes the resources that can be imported into the target tenant.
| Resource | Category | Description |
|---|---|---|
| Dataset | Data Collection | A dataset is the data that is used as input in a quest to build a model. The import in the target tenant includes the definition of the dataset from Infor Data Lake. |
| Groups | Data Collection | The groups bind the multiple datasets together with a unique name. This is a mandatory step that is required for the successful import of the Optimization quest into the target tenant. |
| Quest | Machine Learning | A quest is the flow of activities that build the machine learning model.
The import in the target tenant includes the training and production quest definition. |
| Endpoint | Machine Learning | An endpoint is the deployed machine-learning model (REST API) that is invoked to get real-time predictions.
The import in the target tenant includes the endpoint definition. |
| Custom Algorithm | Machine Learning | A custom algorithm is a user-defined source code that is used as an algorithm to train a machine-learning model.
The import in the target tenant includes the custom algorithm docker image and the source code files package. |
| Quest | Optimization | A quest is the flow of activities that build the optimization model.
The import in the target tenant includes the design and production quest definition. |
| Endpoint | Optimization | An endpoint is the deployed optimization model (REST API) that is invoked to get real-time solutions.
The import in the target tenant includes the endpoint definition. |
| Custom Algorithm | Optimization | A custom algorithm is a user-defined source code that is used to solve the optimization problems.
The import in the target tenant includes the custom algorithm docker image and the source code files package. |