Machine Learning Jobs
Machine learning platforms and services enables you to create, train, deploy, and manage machine learning models on premises, in the cloud, and on edge devices.
The following topics describe job attributes that work with Machine Learning platforms and services:
AWS SageMaker Job
AWS SageMaker enables you to create, train, and deploy machine learning models on premises, in the cloud, and on edge devices.
To create an AWS SageMaker job, see Creating a Job. For more information about this plug-in, see
The following table describes the AWS SageMaker job type attributes.
Attribute |
Description |
---|---|
Connection Profile |
Determines the authorization credentials that are used to connect Control-M to AWS SageMaker, as described in Machine Learning Connection Profiles. Rules:
|
Pipeline Name |
Determines the name of the preexisting AWS SageMaker pipeline used in this job. |
Idempotency Token |
(Optional) Defines the unique ID (idempotency token) that guarantees the job is executed only once. After successful execution, this ID cannot be used again. To allow a re-execution of the job with a new token, replace the default value with a unique ID that has not been used before. Use the RUN_ID, which can be retrieved from the job output. Default: Token_Control-M_for_SageMaker%%ORDERID. |
Add Parameters |
Determines whether to add or change default parameters in the execution of the pipeline. |
Parameters |
Defines the parameters, in JSON format, to add or change, according to the AWS SageMaker convention. The list of parameters must begin with the name of the parameter type. Copy
|
Retry Pipeline Execution |
Determines whether to retry the execution of a pipeline, which you might want to do if a previous execution fails or stops. |
Pipeline Execution ARN |
Defines the Amazon Resource Name (ARN) of the pipeline, which is required to retry the execution of the pipeline. An ARN is a standardized AWS resource address. arn:aws:sagemaker:us-east-1:122343283363:pipeline/test-demo-123-p-ixxyfil39d9o/execution/4tl5r9q0ywpw |
Status Polling Frequency |
Determines the number of seconds to wait before checking the status of the job. Default: 30 |
Failure Tolerance |
Determines the number of times to check the job status before ending Not OK. Default: 2 |
Azure Machine Learning Job
Azure Machine Learning enables you to build, train, deploy, and manage machine learning models on premises, in the cloud, and on edge devices.
To create an Azure Machine Learning job, see Creating a Job. For more information about this plug-in, see
The following table describes the Azure Machine Learning job type attributes.
Attribute |
Description |
---|---|
Connection Profile |
Determines the authorization credentials that are used to connect to Control-M to Azure Machine Learning, as described in Azure Machine Learning Connection Profile Parameters. Rules:
|
Workspace Name |
Determines the name of the Azure Machine Learning workspace for the job. |
Resource Group Name |
Determines the Azure resource group that is associated with a specific Azure Machine Learning workspace. A resource group is a container that holds related resources for an Azure solution. The resource group can include all the resources for the solution, or only those resources that you want to manage as a group. |
Action |
Determines one of the following Azure Machine Learning actions to perform:
|
Pipeline Endpoint ID |
Determines the pipeline endpoint ID, which points to a published pipeline in Azure Machine Learning. |
Parameters |
Defines additional parameters for the pipeline, in JSON format. Copy
For no parameters, type {}. |
Compute Name |
Defines the name of the compute function. |
Compute Action |
Determines one of the following compute actions to perform:
|
Status Polling Frequency |
Determines the number of seconds to wait before checking the status of the job. Default: 15 |
Failure Tolerance |
Determines the number of times to check the job status before ending Not OK. Default: 2 |
OCI Data Science Job
OCI Data Science is an Oracle Cloud Infrastructure (OCI) platform, that enables you to build, train, deploy, and manage machine learning (ML) models using Python and open source tools.
To create an OCI Data Science job, see Creating a Job. For more information about this plug-in, see Control-M for OCI Data Science.
The following table describes OCI Data Science job attributes.
Attribute |
Description |
---|---|
Connection Profile |
Determines the authorization credentials that are used to connect Control-M to OCI Data Science Services, as described in OCI Data Science Connection Profile Parameters. Rules:
Variable Name: %%INF-ACCOUNT |
Actions |
Determines one of the following OCI Data Science actions:
|
Parameters |
Defines the parameters for the following actions, in JSON format, for example:
|
Model Deployment ID |
(Delete Model Deployment) Determines the OCID of the model deployment that is deleted. |
Notebook Session ID |
(Delete Notebook Session) Determines the OCID of the notebook session that is deleted. |
Status Polling Frequency |
Determines the number of seconds to wait before checking the job status. Default: 60 |
Failure tolerance |
Determines the number of times to check the job status before ending Not OK. Default: 2 |