ModelOps for Structural Analytics

Over the past few years, MLOps has emerged as an essential discipline for organisations deploying AI and machine-learning models at scale. Borrowing from DevOps, it explores the infrastructure, operating model, and tooling required to manage ML models as enterprise assets.

This has helped industrialise AI/ML pipelines — but it only solves part of the problem.

In most banks and financial institutions, the models that matter most for regulatory filings, risk management, accounting, and forecasting are not machine-learning models. They are deterministic, rules-based and highly structured. This domain — which we refer to as structural analytics — differs fundamentally from AI/ML, and those differences have important implications for how ModelOps should work in practice.

What Makes Structural Analytics Different

1. Model Construct:

In AI/ML, models “emerge” organically from the underlying data via a training process.

In structural analytics, models are more consciously constructed, pieced together to comply with regulation, established practice, and business logic. Often this involves working within a fixed structure where an overall model is built from subsidiary components that can evolve in a semi-independent way.

2. Oversight Levels: 

Structural analytics can face intense scrutiny — from model risk management teams to regulators and auditors. Their operating environments need to produce not only correct results, but also clear, defensible evidence of how results were generated. This level of oversight places unique demands on the operational framework.

3. Analytical Use:

Oftentimes, structural analytic models are not single-purpose components in an automated workflow. They support a spectrum of activities — from production reporting to ad-hoc exploration. Teams routinely:

• analyse drivers of results,

• run sensitivities,

• explore impacts on sub-populations,

• check consistency with business intuition or historical trends.

The requirement to use the in-governance model for both controlled reporting and exploratory analytics is common in structural analytics.

What This Means For ModelOps 

Many MLOps principles remain valuable, but structural analytics requires adaptations, rather than a like-for-like transfer of MLOps thinking.

1. Portability:

Structural models need to be run in multiple environments and teams - from development to production, and from experimentation to validation. True model portability implies not just the ability to run the same parent model in different environments, but also the ability to easily recompile that calculation using different versions of its child models. Typically, this means that containerisation – a preferred approach for solving portability in machine learning – is insufficient for the more complex structural analytics use-cases.

2. Documentation:

Governance teams need clear documentation describing how models are deployed, parameterised, and executed. For structural analytics, this is not just about versioning code and parameters; it requires robust process documentation that articulates the inputs and outputs of each ‘production run’. Efficient ModelOps solutions should automate this documentation as models are deployed and used, rather than relying on manual efforts.

3. Repeatability:

The ability to recreate, analyse, and compare historical model runs is one of the most challenging features of structural analytics but one that is often required in areas such as capital, impairment, balance sheet management and scenario analysis. Often this requires painstaking manual work to recreate prior configurations from paper documentation. To automate this capability, requires solutions that apply systemic versioning across entire workflows — not just model code, but data, configuration, and business logic.

4. Experimentation:

Users need the ability to test new assumptions or scenarios without disrupting production deployments. This isn’t the free-form experimentation typical in data science. It’s controlled experimentation within the constraints of the model’s structure — with proper audit trails and separation of duties. Efficient ModelOps also includes the ease with which production models can be used in non-production contexts, to answer business questions.

Conclusion: ModelOps for Structural Analytics        

The highly governed models used in structural analytics may not be ML/AI — but they are critical business assets and need at least the same level of operational discipline. A tailored ModelOps approach for structural analytics should combine the rigour of MRM, the technical discipline of MLOps, and the realities of how these models are built and used. Without it banks will remain stuck with outdated tools and processes that comply on paper, but lack real control, agility, and efficiency.

The institutions that modernise this layer will gain not just compliance, but real operational advantage.