Who’s Accountable for AI and its Risks? Why Enterprise CEOs Need to Assign AI Ownership Now
Webinar | Tuesday, April 30th | 1pm ET
Close this search box.

How ModelOp Center Automates Model Life Cycles and Why It Matters (part 1 of 2)

ModelOp Center automates Model Life Cycles part 1

by ModelOp Product Team –  3 minutes read

This blog describes how ModelOp Center automates Model Life Cycles (MLC) to drive repeatability, business and technical auditability and accountability for a robust ModelOps (and MLOps) program.


ModelOp Center automates operations related to the deployment, monitoring, and governance of models so that you can get them into service quickly, keep track of how each model is performing (both technical and business KPIs), and have easy access to the entire history of each model. For a large enterprise, there are hundreds or thousands of models, each of which has differing business requirements and different pathways to production. ModelOp Center MLC-centricity provides flexibility with how you manage and automate portions of a model’s life cycle to meet the disparate needs across groups — all in a central, governed location.

There are two core concepts to how ModelOp Center achieves enterprise-scale automation and repeatability: the MLC Manager and the MLC Process. The subsequent sections provide more detail on each, and then dive into several scenarios of how to leverage the MLC Manager and MLC Processes.

MLC Manager

The MLC Manager is a low-code automation framework that executes, monitors, and manages MLC Processes. The MLC Manager is built on top of Camunda: a leading Java-based framework supporting Business Process Model and Notation (BPMN) for workflow and process automation. The MLC Manager is the answer to a number of obstacles faced by teams:

  • 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.
  • 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 model.
  • Allows to define a per-model path to production, inclusive of business processes
  • 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 requirements, or rapid deployment internal-use-only models that require a minimum of process.

A typical ModelOp Center implementation will have more than one MLC Process. Each MLC Process is defined in any BPMN compliant editor, such as Camunda Modeler, as a BPMN file.

MLC Processes leverage the standard elements of a Camunda BPMN along with custom delegates that interface with ModelOp Center. This allows the flexibility to orchestrate complex operations within ModelOp Center. The common entities within an MLC Process include:

  • Signal events – events that initiate the MLC Process. 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 and Model Deployments.
  • Gateway – decision logic gates that control the flow based on information in the process, such as model metadata, test results, etc.

The automated operations within an MLC Process include collecting key metrics to help calculate Key Performance Indicators (KPI), such as how long it takes to get Models into Business or get changes approved.

For more details on the standard elements of BPMN, you can see the full documentation of Camunda at https://camunda.com/bpmn/reference/.



You might also enjoy

AI Regulations: What to Know & What to Do Now

Global, federal, and state-level governments are moving quickly to implement AI regulations. While reading this, you may be asking, “If I want to use AI, what do I need to do now to prepare my organization now?”

Get the Latest News in Your Inbox

Further Reading