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Reduces the time it takes to get a model from the model factory into production by defining a consistent methodology within your business to move the model through each required step, and track its progress throughout your organization.
Ensures that all models in production are producing optimal results and within compliance rules
Scales the functions necessary to manage the hundreds or thousands of models across the enterprise, controlling the most important tasks and processes for a variety different models.Incorporates visualization tools to display real-time status and availability of system processes and resources.
MLC Process
The MLC Process encodes and automates a set of steps in a model’s life cycle, which can range from model registration, to submitting models for full productionization, to continuous production testing, and eventual retirement. The MLC Manager executes and monitors each MLC Process, and automatically captures metadata and information about the model’s journey through the MLC Process.
An MLC Process can apply to an individual model or a set of models, using common criteria such as business unit, model language, or the model framework they employ. Regardless, the MLC Process provides the consistent methodology for managing the various pathways of a model’s journey in an enterprise, across all models and all groups. This could include highly regulated models that require strict government requirementsregulatory oversight, or rapid deployment internal-use-only models that require a minimal process.
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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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For more details on the standard elements of BPMN 2.0, you can see the full documentation of Camunda at https://camunda.com/bpmn/reference/.
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In fact, the ModelOp Command Center has several UI features that leverage MLC Processes under the hood, including submitting the model creating a snapshot which begins the process for productionization from the Model Details screen or executing creating a Batch Job from the Runtimes screenJobs page, which handles executing the job. As you one can already tellsee, there are many ways to automatically do a variety of different things tasks with MLC Processes. The following provides more details of these typical processes.
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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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Next Article: Deploy a Model into a ModelOp Runtime >