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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. Mass Ingestion Files REST API
  7. Mass Ingestion Streaming 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

Registering a machine learning model

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\"}]}" } }

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