Control-M for Azure Machine Learning

Azure Machine Learning enables you to build, train, deploy, and manage machine learning models on premises, in the cloud, and on edge devices.

Control-M for Azure Machine Learning enables you to do the following:

• Execute an Azure Machine Learning endpoint pipeline and stop, start, restart, or delete a host or cluster.

• Manage Azure Machine Learning credentials in a secure connection profile.

• Connect to any Azure Machine Learning endpoint.

• Introduce all Control-M capabilities to Control-M for Azure Machine Learning, including advanced scheduling criteria, complex dependencies, Resource Pools, Lock Resources, and variables.

• Integrate Azure Machine Learning jobs with other Control-M jobs into a single scheduling environment.

• Monitor the status, results, and output of Azure Machine Learning jobs.

• Attach an SLA job to the Azure Machine Learning jobs.

• Run 50 Azure Machine Learning jobs simultaneously per Agent.

Setting up Control-M for Azure Machine LearningLink copied to clipboard

This procedure describes how to deploy the Azure Machine Learning plug-in, create a connection profile, and define an Azure Machine Learning job in Control-M SaaS and Automation API.

Before You Begin

• Verify that Automation API is installed, as described in Setting Up the API.

• Verify that Agent version 9.0.21.080 or later is installed.

1. On the Agent host, run one of the following commands to set the Java environment variable:

   • Linux:

     • Bourne shell/bash: export BMC_INST_JAVA_HOME=<java_11_directory>

     • csh/tcsh: setenv BMC_INST_JAVA_HOME <java_11_directory>

   • Windows: set BMC_INST_JAVA_HOME="<java_11_directory>"

2. Run one of the following API commands:

   • To install, type one of the following provision image commands:

     • Linux: ctm provision image ZML_plugin.Linux

     • Windows: ctm provision image ZML_plugin.Windows

   • To upgrade, type the following command:

     ctm provision agent::update

3. Create an Azure Machine Learning connection profile in Control-M SaaS or Automation API, as follows:

   • Control-M SaaS: Create a Centralized Connection Profile with Azure Machine Learning Connection Profile Parameters

   • Automation API: ConnectionProfile:Azure Machine Learning

4. Define an Azure Machine Learning job in Control-M SaaS or Automation API, as follows:

   • Control-M SaaS: Create a Job with Azure Machine Learning Job parameters

   • Automation API: Job:Azure Machine Learning

Change LogLink copied to clipboard

The following table provides details about changes that were introduced in new versions of this plug-in:

Plug-in Version	Details
1.0.01	Internal changes to post-API calls
1.0.00	Initial version