ModelOps

What Is ModelOps?

In view of the unique nature of models and their life cycles, many large enterprises that were not “born digital” have experienced significant difficulty moving models from development into production while ensuring ongoing monitoring and governance.

The response to this challenge is the development of a new discipline known as ModelOps.

Enterprise ModelOps: A Strategic Capability

ModelOps is an organizational capability that enables large enterprises to scale and govern their AI initiatives. While some form of ModelOps is required in any organization, Enterprise ModelOps is essential for large, complex businesses seeking to broadly leverage models across operations.

ModelOps Defined by Gartner

ModelOps (AI model operationalization) is primarily focused on the governance and life cycle management of AI and decision models. This includes machine learning, knowledge graphs, rules, optimization, linguistic, and agent-based models.

Core capabilities include:

• managing development environments
• maintaining a model repository
• champion-challenger testing
• CI/CD integration

ModelOps supports the retuning, retraining, and rebuilding of models, ensuring a continuous flow between development, operationalization, and maintenance.

It gives business domain experts the autonomy to assess model quality and decide on model promotion or demotion for inferencing—without full dependency on data scientists or engineers.

(Source: Gartner, “A Guidance Framework for Operationalizing Machine Learning”, Soyeb Barot, May 2020)

The Business Value of ModelOps

An effective ModelOps capability accelerates AI initiatives, eliminates friction, and reduces excess costs. It empowers both professional and citizen data scientists while protecting the enterprise from regulatory and operational risks.

Core Capabilities of ModelOps

Lifecycle Definition and Automation

• A CAD-like system to define the end-to-end lifecycle of each model
• A business process execution engine to automate and visualize the full lifecycle
• A central model catalog capturing metadata, technical specs, KPIs, and approvals

Deployment and Infrastructure Integration

• Encapsulation tools for deploying models across on-prem, private cloud, public cloud, and hybrid environments
• Model pipeline creation tools that link multiple models and data sources

Performance Monitoring and Testing

• Instrumentation for tracking technical and business performance in production
• Facilities for A/B testing and champion/challenger evaluation
• Alerting for technical or business degradation

Compliance and Audit Support

• Tools for compliance testing and audit-ready report generation
• Support for explainability, bias testing, and regulatory frameworks

Business Integration and Transparency

• Interfaces to BI tools and analytics platforms to align model outputs with business KPIs
• Visibility for business leaders into AI investments and outcomes

Enterprise Systems Alignment

• Integration with code management, ticketing, performance monitoring, security, and identity management
• Support for non-AI models including rule-based, optimization, and hybrid model pipelines

Who ”Owns” ModelOp?

The appropriate organizational owner for ModelOps can vary for different types of organizations.

For large enterprises that have a central IT organization responsible for providing IT operations across the company, ModelOps is necessarily the purview of the CIO, ofter provided as a Shared Service.

There are several reasons for this:

• The CIO’s organization operates the company’s production IT infrastructure and the ITOps team is the only group authorized to manipulate production systems. There’s no provision for a data scientist to release, say, a retrained model into a production IT system.
• The ITOps team is the only group within the company with the organizational resources and skills required to operate 24x7 business-accountable systems and applications.
• The CIO organization includes enterprise AI architects that can design and implement end-to-end model life cycles.
• The CIO organization operates the systems that provide visibility and control for the line of business and compliance organizations.
• The CIO organization is responsible for implementing the organization’s digital transformation and cloud journeys.
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Thus, the Enterprise AI Architect and the ModelOps team are the primary operators of the ModelOps solution, but all other stakeholders in the Model Life Cycle interact with the system, even if “invisibly” via interfaces to their primary tools (i.e. the data science workbenches, BI tools, DevOps tools, risk management systems, etc.).

What’s the Best Way to Implement ModelOps?

Implementing a highly performance, enterprise-grade ModelOps capability is a transformational process that requires significant planning and participation among disparate organizations across the company.

The most critical factor for success is to develop and communicate a common vision across the company of models as critical, first-class enterprise assets that are fundamental to the organization’s ability to compete and drive profitable growth.

Although implementing an effective ModelOps program is a project with significant depth and scope, it is neither necessary nor desirable to address it as a monolithic project. The best way is to start with a few models, or even a single model, and implement, test and revise in stages.

Some steps that are especially important at the outset include:

• Designate the appropriate member(s) of the CIO’s organization to lead the ModelOps effort.
• Designate a lead Enterprise AI Architect
• Identify candidate model(s) for initial implementation and identify all of the key stakeholders from all parts of the organization.
• Identify the key business KPIs, operational requirements, the regulatory and compliance requirements and the reporting requirements.
• With guidance and support from the Enterprise AI Architect, design end-to-end life cycle(s) for the models.
• Select and Enterprise ModelOps solution to provide the framework for the ModelOps program.
• Integrate the ModelOps solution with the data science tools, the DataOps, DevOps and ITOps systems, the business applications and analytics tools, risk management systems and the enterprise IT shared services stack.
• Create the reporting dashboards and implement the alerting systems and triggers.
• Run the model through the life cycle, monitor and measure, and track key parameters (such as ModelDebt) against targets.
• Identify learnings, adjust the processes as necessary, and bring on more models.
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