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.

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.

Rules:

  • Characters: 1−30

  • Case Sensitive: Yes

  • Invalid Characters: Blank spaces.

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 
{ "Name": "string", "Value": "string" }

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.

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.

Rules:

  • Characters: 1−30

  • Case Sensitive: Yes

  • Invalid Characters: Blank spaces.

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:

  • Trigger Endpoint Pipeline: Starts a pipeline.

  • Compute Management: Stops, starts, restarts, or deletes a computer or a cluster.

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 
{
    "ExperimentName": "test",
    "DisplayName":"test1123"
}

For no parameters, type {}.

Compute Name

Defines the name of the compute function.

Compute Action

Determines one of the following compute actions to perform:

  • Start: Starts a computer or a cluster.

  • Stop: Stops a computer or a cluster.

  • Restart: Restarts a computer or a cluster.

  • Delete: Deletes a computer or a cluster.

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