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  1. Preface
  2. Informatica Intelligent Cloud Services REST API
  3. Platform REST API version 2 resources
  4. Platform REST API version 3 resources
  5. Data Integration REST API
  6. File Ingestion and Replication REST API
  7. Streaming Ingestion and Replication REST API
  8. Model Serve REST API
  9. RunAJob utility
  10. ParamSetCli utility
  11. REST API codes
  12. REST API resource quick references

REST API Reference

REST API Reference

Model Serve resource quick reference

Model Serve
resource quick reference

The following list contains the syntax and a brief description of the actions you can perform with the
Model Serve
resources:
Get descriptions of all quick start models
To get a list of the names and descriptions of all of the quick start models, use the following URI:
/mlops/api/v1/modelhub/models
Monitor all quick start models
To get the statuses of and information about all of the quick start models, use the following URI:
/mlops/api/v1/modelhub/monitor
Get information about a quick start model
To get the status of and information about a single quick start model, use the following URI:
/mlops/api/v1/modelhub/preBuiltModel?name=<model name>
Use
TEXT_TRANSLATION
or
IMAGE_CLASSIFICATION
as the model name.
Get the code template file
To get the code template file that you need to define a machine learning model, use the following URI:
/mlops/api/v1/model/template/custom
Upload a model file
When you upload a model file, first generate a model file ID and then use the ID to upload the file.
To generate a model file ID, use the following URI:
/mlops/api/v1/model/upload/generateId
To upload or update a model file, use the following URI:
/mlops/api/v1/model/upload/<model file ID>
Register a machine learning model
Register a machine learning model after you upload the model file that defines the machine learning algorithm.
To register a machine learning model, use the following URI:
/frs/v1/Projects('<model ID>')/Documents
Edit a machine learning model
To edit a registered machine learning model, use the following URI:
/frs/v1/Documents('<ID>')
Download a model file
To download the model file from a registered machine learning model, use the following URI:
/mlops/api/v1/model/download/<ID>
Create a model deployment
To create a model deployment based on a registered machine learning model, use the following URI:
/frs/v1/Projects('<deployment ID>')/Documents
Edit a model deployment
To edit a model deployment, use the following URI:
/frs/v1/Documents('<deployment ID>')
Monitor deployments
To monitor the status of all model deployments in your organization, use the following URI:
/mlops/api/v1/deployment/monitor
To monitor the status of one model deployment, use the following URI:
/mlops/api/v1/deployment/monitor/<deployment ID>
Control a quick start model or model deployment
You can start a quick start model or model deployment to make it available for predictions, restart it while it's running, and stop it to release the resources.
To start a deployment, use the following URI:
/mlops/api/v1/deployment/control/<deployment ID>/start
To restart a deployment, use the following URI:
/mlops/api/v1/deployment/control/<deployment ID>/restart
To stop a deployment, use the following URI:
/mlops/api/v1/deployment/control/<deployment ID>/stop
Generate predictions
To generate predictions from an available quick start model or model deployment, use the following URI:
/mlops/api/v1/deployment/request/<deployment ID>
Delete assets
To delete a machine learning model or model deployment, use the following URI:
/frs/api/v1/Documents('<ID>')

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