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.

Control-M for Azure Machine Learning Compatibility

The following table lists the prerequisites that are required to use the Azure Machine Learning plug-in, each with its minimum required version.

Control-M for Azure Machine Learning is supported on Control-M Web and Control-M Automation API, but not on Control-M client.

To download the required installation files for each prerequisite, see Obtaining Control-M Installation Files.

Setting up Control-M for Azure Machine Learning

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 Web and Automation API.

NOTE: Integration plug-ins released by BMC require an Application Integrator installation at your site. However, these plug-ins are not editable and you cannot import them into Application Integrator. To deploy these integrations to your Control-M environment, you import them directly into Control-M using Control-M Automation API.

Before you Begin

Verify that Automation API is installed, as described in Automation API Installation.

Begin

1. Create a temporary directory to save the downloaded files.

2. Download the Azure Machine Learning plug-in from the Control-M for Azure Machine Learning download page in the EPD site.
3. Install the Azure Machine Learning plug-in via one of the following methods:
   • (For version 9.0.21.or later) Use the Provision service of Automation API:
     1. As an administrator on the Control-M/EM Server, store the downloaded zip file in the following location.
        Within several minutes, the zip file is available in all Control-M interfaces associated with the Control-M/EM.
        
        • Linux: $HOME/ctm_em/AUTO_DEPLOY
        • Windows: <EM_HOME>\AUTO_DEPLOY
     2. As an application user on the Agent machine, run the provision image command, as follows:
        • Linux: ctm provision image ZML_plugin.Linux
        • Windows: ctm provision image ZML_plugin.Windows
   • (For versions lower than 9.0.21) Use the Deploy service of Automation API, as described in deploy jobtype.
4. Create an Azure Machine Learning connection profile in Control-M Web or Automation API, as follows:
   • Web: Creating a Centralized Connection Profile with Azure Machine Learning Connection Profile Parameters
   • Automation API:  ConnectionProfile:Azure Machine Learning
5. Define an Azure Machine Learning job in Control-M Web or Automation API, as follows:
   • Web: Creating a Job with Azure Machine Learning Job parameters
   • Automation API: Job:Azure Machine Learning