To deliver lasting value, businesses must continuously monitor, manage, and improve these models. This is where ModelOps—the practice of governing the full lifecycle of AI models—plays a critical role.
Why Model Governance Matters
Once in production, ML models impact decisions that drive operations, influence customer experiences, and affect financial outcomes. Without governance, these models can drift, fail silently, or produce inaccurate results. Poor oversight can result in regulatory non-compliance, inefficiency, and reputational risk. Model governance ensures models are reliable, accountable, and aligned with business goals.
The Four Perspectives of Model Monitoring
Data Science Perspective
Operational Perspective
IT teams track system metrics such as CPU usage, memory, and network load. Key indicators include latency(delay in processing) and throughput (volume of data processed). These metrics help maintain performance and efficiency.
Cost Perspective
Measuring records processed per second is not enough. Businesses should monitor records per second per cost unit to assess return on investment. This helps determine if a model continues to deliver business value.
Service Perspective
Service Level Agreements (SLAs) must be defined for analytic workflows. These include time to deploy, retrain, or respond to performance issues. Meeting SLAs ensures reliability and stakeholder satisfaction.
The Rise of ModelOps
ModelOps extends beyond machine learning operationalization (MLOps). It governs the entire lifecycle of all AI models—ML, rules-based, optimization, natural language, and others. According to Gartner, ModelOps is central to scaling AI in the enterprise. It enables:
- Version control, traceability, and auditability of models
- Automated testing and validation (champion/challenger frameworks)
- Rollback and redeployment workflows
- Risk assessments and compliance tracking
- Cross-functional collaboration across business, IT, and data teams
FINRA Case Study: Governance in Action
The Financial Industry Regulatory Authority (FINRA) offers a real-world example of model governance at scale. FINRA processes over 600 billion transactions daily. With responsibility for regulating 3,300 securities firms and over 620,000 brokers, governance is crucial.
Key practices at FINRA include:
- A centralized governance framework across decentralized teams
- Real-time monitoring of model performance and drift
- SLAs for model deployment and retraining timelines
- Cross-training staff to foster collaboration between business and tech teams
- Risk-based model lifecycle management
Their approach emphasizes that governance is not an afterthought—it begins with project initiation and continues through post-deployment monitoring.
Enabling ModelOps with Technology
AI governance platforms like ModelOp Center help organizations operationalize governance. These tools integrate with existing development environments, IT systems, and business applications to manage the entire AI lifecycle.
With ModelOp Center, businesses can:
- Cut time-to-decision by 50%
- Improve model-driven revenue by up to 30%
- Reduce compliance and performance risks
These outcomes are possible through end-to-end orchestration, automated monitoring, and unified visibility into all models.
Conclusion: Start Early, Scale Smart
To unlock the full value of AI, organizations must treat ModelOps as a core business function. This means creating clear roles, building cross-functional workflows, and implementing tools to monitor, test, and scale models responsibly. As with DevOps and SecOps, ModelOps is becoming essential for digital maturity.
Companies that invest in governance from the start gain competitive advantage by reducing risk, improving decision accuracy, and accelerating innovation.
