ModelOps is a holistic approach to enable the smooth operationalization of ML models to deliver expected business value to an enterprise.

What is ModelOps?

ModelOps is a holistic approach to governance and life cycle management of machine learning models so that they are deployed faster and drive desired business value. It brings a set of organizational capabilities to accelerate the operationalization of all AI and decision models. 

In its ModelOps definition, Gartner includes a wide range of AI and decision models, rules, knowledge graphs, optimization, linguistic and agent-based models. It also covers core capabilities such as continuous integration/continuous delivery (CI/CD), champion-challenger testing, model development environments, model versioning, model store, and rollback. 

ModelOps considers models' journey from the research phase followed by testing, validation, and deployment phases with scaled accuracy in predictions delivered. It also emphasizes ongoing monitoring and retraining models for persistent and scaled performance.

Ideal ModelOps solutions offer a collection of tools, technologies, and best practices to deploy and manage ML models. ModelOps is an extension to MLOps with its users spanning across CIO/IT department, Enterprise Risk, Enterprise IT, or Line of Business Operations. While DevOps is concerned with traditional software, MLOps applies to ML models, and ModelOps becomes a superset of MLOps to operationalize all AI models. 


Why ModelOps?

ModelOps helps bridge the gap between ML teams, application teams, and infrastructure owners. With ModelOps infrastructure in place, enterprises enhance transparency, governance, and auditability to scale enterprise AI initiatives. It helps reduce infrastructure and operational costs while automating the process of model monitoring. 

Modern enterprises consider ModelOps to ensure: 

Compliance requirement: By enabling better governance, rapid deployment, and automated performance monitoring across platforms, ModelOps help adhere to business and regulatory compliance requirements. 

Team Collaboration: ML projects involve multiple teams working in silos - from research to deployment to monitoring. ModelOps breaks these silos and supports a collaborative team environment to scale AI across the enterprise. 

Unified approach: ModelOps provides a unified approach to managing diverse ML project workflows, auditing, model governance, and performance tuning.

Better risk management: ModelOps plays a crucial role in ML risk management. It enables continuous monitoring and identifying risks earlier to avoid significant damage.  

Next Steps

Despite AI's buzz, not all enterprises can drive expected returns from their AI investments. Its main reasons include

  • Manual workflows.
  • Complex technologies.
  • Data quality issues.
  • Teams working in silos.
  • Lack of appropriate ML monitoring.
  • Weak AI strategy. 

Fortunately, modern toolsets and proven approaches help you conquer these challenges. ModelOps is one such approach to conquer ML model operationalization challenges. It is driven by a wide range of platforms and tools that are readily available. Applying these utilities helps ML teams build a model workflow, monitor pipelines, and ensure seamless governance.

ModelOps brings multifold benefits to the organization, such as faster GTM, risk mitigation, and simplified workflows.

Further Reading

ModelOps: A buzzword or a strategic approach for operationalising ML?

What Is ModelOps and How Is It Different From MLOps?

ModelOps Is The Key To Enterprise AI


Does ModelOps Represent A New Horizon For MLOps?

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