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Registering a machine learning model

Use the Documents resource to register a machine learning model after you upload the model file.

POST request

To register a machine learning model, include the project or folder ID in the URI. Use the following URI:

/frs/v1/Projects('<project ID>')/Documents

Include the following fields in the request:

Field	Type	Description
name	String	Machine learning model asset name.
description	String	Optional. Description of the machine learning model.
documentType	String	Use MLOPS_MODEL .
nativeData	Object	Blob object that defines the machine learning model.

Include the following fields in the nativeData object:

Field	Type	Description
name	String	Machine learning model asset name.
framework	String	Framework used to create the machine learning model. Use one of the following values: Python PyTorch TensorFlow
framework_type	String	Internal type of the framework. For a Python model, use Custom . For a PyTorch or TensorFlow model, use Standard .
file_id	String	Model file ID generated when you upload the model file.
file_name	String	Name of the model file. The name must match the file name you used to generate the model file ID.
size	String	Size of the model file in GB.
file_type	String	Use zip .
inputField		Input fields that the machine learning model expects. Include at least one input field.
name	String	Name of the field.
dataType	String	Data type of the field.
shape	String	Required for array data types. Shape of the array. For example, [2,3] .
outputField		Output fields that the machine learning model returns. Include at least one output field.
name	String	Name of the field.
dataType	String	Data type of the field.
shape	String	Required for array data types. Shape of the array. For example, [2,3] .

POST response

Returns a summary of the machine learning model.

The following table describes the attributes in the machine learning model summary:

Field	Description
id	ID of the machine learning model.
name	Machine learning model name.
description	Description of the machine learning model.
owner	ID of the user who owns the machine learning model.
createdBy	ID of the user who created the machine learning model.
lastUpdatedBy	ID of the user who last edited the machine learning model.
lastAccessedBy	ID of the user who last viewed the machine learning model.
createdTime	Date and time that the machine learning model was created.
lastUpdatedTime	Date and time that the machine learning model was last edited.
lastAccessedTime	Date and time that the machine learning model was last viewed.
expiresBy	Date and time that the machine learning model will expire.
parentInfo
parentID	ID of the project or space that contains the machine learning model.
parentName	Name of the project or space that contains the machine learning model.
parentType	Type of object that contains the machine learning model.
documentType	Type of asset. A machine learning model returns MLOPS_MODEL .
contentType	Returns Binary .
documentState	Returns VALID if the model is valid or INVALID if there are errors in the configuration.
aclRule
subcontainerCount
customAttributes
repoInfo
repoParams
repoPath
repoHandle
frsPath
isSourceControlled	Returns false .
checkOutById	Returns null .

Example

{
  "name": "model1",
  "description": "desc",
  "documentType": "MLOPS_MODEL",
  "nativeData": {
    "documentBlob": "{\"name\":\"model1\",\"framework\":\"Tensorflow\",\"framework_type\":\"Standard\",\"file_id\":\"012\",\"file_name\":\"model1\",\"size\":\"1048576\",\"file_type\":\"zip\",\"inputField\":[{\"name\":\"input1\",\"dataType\":\"Array of integers\",\"shape\":\"[2,3]\"},{\"name\":\"input2\",\"dataType\":\"Base64 String\"}],\"outputField\":[{\"name\":\"output1\",\"dataType\":\"Boolean\"}]}"
  }
}

Machine learning models

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