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:

• Connect to any Azure Machine Learning endpoint from a single computer with secure login, which eliminates the need to provide authentication.
• Integrate Azure Machine Learning jobs with other Control-M jobs into a single scheduling environment.
• Execute an Azure Machine Learning endpoint pipeline and stop, start, restart, or delete a computer or cluster.
• Monitor the status, results, and output of Azure Machine Learning jobs in the Monitoring domain.
• Attach an SLA job to your Azure Machine Learning jobs.
• Introduce all Control-M capabilities to Control-M for Azure Machine Learning including advanced scheduling criteria, complex dependencies, Resource Pools, Lock Resources, and variables.
• Run 50 Azure Machine Learning jobs simultaneously per Agent.

Setting up Control-M for Azure Machine Learning

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

Begin

1. On the Agent host, set the Java environment variable by running one of the following commands through a command line:
   
   • 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:
   • For a fresh installation, use the provision image command:
     • Linux:  ctm provision image ZML_plugin.Linux
     • Windows: ctm provision image ZML_plugin.Windows
   • For an upgrade, use the following command:
     ctm provision agent::update
3. Create an Azure Machine Learning connection profile in Helix Control-M or Automation API, as follows:
   • Helix Control-M: Creating 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 Helix Control-M or Automation API, as follows:
   • Helix Control-M: Creating a Job with Azure Machine Learning Job parameters
   • Automation API: Job:Azure Machine Learning