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Signal events - events that initiate the MLC Process or trigger an action to occur from within an MLC. These can be triggered on events such as when a model is changed or based on a timer.
Tasks - there are a variety of tasks within an MLC Process:
User tasks - manual tasks for specific users to perform, such as approvals. These pause the progress of the workflow until completed.
External service calls - used to integrate and interact with other systems.
Script tasks - runs custom code including inline Groovy. Typically, you utilize variables and model metadata to determine parameters for calls to ModelOp Center.
ModelOp Center calls - specific calls to ModelOp Center that automate interactions with the model including Batch Jobs (see: Model Batch Jobs and Tests) and Model Deployments.
Gateway - decision logic gates that control the flow based on information in the process, such as model metadata, test results, etc.
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MLC Processes can automate the productionization of a model, regardless of whether the path to production is simple or complex. For example, you can use an MLC Process to deploy a newly registered model into your QA runtime, run the model through a series of tests, trigger an automated security scan, and seek appropriate approvals before it is deployed into Production. MLC Processes can be created in a flexible manner to meet the needs of your team. They can be configured to automatically locate an available runtime that is compatible with the current model, or a specific group of runtimes can be targeted by tags. See this article for more details on how this is accomplished. The example in On Model Changed includes these deployment pieces.
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After the initial deployment, it’s important to have a way to rapidly retrain or trigger the refresh of a model to ensure it is performing optimally. Retraining can be automated within an MLC process to run on a schedule or when new labeled data is available. Using the same MLC process, the new candidate model can be compared against the current deployed model using a Champion/Challenger Model Comparison. Finally, the MLC Process can automate the steps required for Change Management including re-testing and approvals. The example On Model Changed demonstrates how you can build these operations into an MLC Process.
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You can monitor models using MLC Processes by automatically running Model Batch Jobs and Tests on a model. You can run Batch Jobs on a schedule or based on new labeled or ground truth data becoming available. For example, you can run a Batch Metrics Job to calculate the statistical performance and/or determine if the model has started to produce ethically biased predictions, and then use decision criteria to determine which action to take. A common pattern is to generate an alert into ModelOp Center for the ModelOp Support Team to triage.
For more details see:
Sample MLC Processes
This section describes some specific examples of MLC Processes in detail.
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4. Verify that your new MLC Process is registered with MLC Manager. Go to the Command Center and click the Models icon in the left column.
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