Introducing ModelOps to Operationalize AI

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ModelOps Key to Successfully Operationalize AI

Model Decision-Making Processes paved the way for Decision Intelligence with ModelOps. Forrester's report on data science use cases and predictive analytics vendors highlighted that data scientists often complain about their models rarely being deployed. The primary challenge lies in the organizational chaos surrounding the building and scaling of ML models into applications. AI, analytics models, and decision models differ from software code as they require model management through MLOps Framework and ModelOps. Organizations must adopt ModelOps governance, MLOps best practices, AI security, and responsible AI framework.

What is ModelOps?

Gartner describes ModelOps as a system that focuses on managing and governing a variety of AI and decision models, like machine learning, knowledge graphs, rules, optimization, linguistic, and agent-based models. Essentially, ModelOps is at the heart of every organization's enterprise AI strategy.

"According to Forrester analysts Mike Gualtieri and Kjell Carlsson, there are three key ModelOps capabilities that organizations need to succeed with AI on a large scale"

For starters, we need to deploy and serve ML models
ModelOps provides monitoring capabilities to ensure ML models don’t go off the rails
ML lifecycle must be managed

Now, you must be wondering how ModelOps is different from MLOps. Well, it's simple: MLOps focuses only on the Operationalization of ML models, while ModelOps focuses on the operation of all AI and Decision Models. We will cover this in detail in the latter half of the article.

Importance of ModelOps

Organizations also struggle with analytics operationalization because they lack a formal system to organize resources through analytics, IT, and the organization. Since data alone doesn't drive the business, decisions do. They are making decisions that impact organizations each day. Since analytically driven decisions are smarter choices, it helps them make the right choices every time, even while making thousands or millions of them every day, by integrating analytics into the decision-making processes. This includes the operationalization of scale analytics.

The number of models - To account for business process variations, personalization, and specific customer groups, each organization will need to handle hundreds of models.
Technology Complexity - The fast and continuing innovation in data & analytics contributes to the unmanageable difficulty for even the most expert IT teams.
Regulatory Compliance - As the use of AI spreads across markets, adhering to strict and ever-increasing models, regulatory criteria become more challenging.
Organization Silos - Scaling AI in trends that are in businesses needs industry, technology, and data to come together. To ensure its free flow through the enterprise, the organizational data needs to be opened. This cannot occur in a siloed work culture, and organizations must develop an interdisciplinary team to push AI in the organization.

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Key Technologies in ModelOps

Multi-Cloud data and AI architecture

Investing in data-driven, cloud-based infrastructure, and microservices will help enterprises to enable the accelerated growth and rollout of software, as well as workload and model portability through several clouds. Think beyond machine learning models: To achieve the full benefits of a model-based approach, make use of AI models outside machine learning, such as organizational models, optimization models, and transformational models that will be helpful for enterprises.

AI governance at scale

It will help ensure that models are governed, trusted, and explainable and contribute to the creation of implementations that are consistent with market priorities and legislation relevant to compliance and scalable to adapt to evolving business needs on a scale.

How to operationalize ModelOps?

Enterprises need to monitor the performance of the ModelOps software for the first phase of a ModelOps process. Since ModelOps is a growth, testing, rollout, and monitoring cycle, it will only be successful if it progresses against delivering the organization's size and accuracy. Then need to track each model's success at the organizational stage.

For models (or model classes), set accuracy goals and monitor them for dimensions such as drift and degradation through development, validation, and deployment.
Identify market indicators that are influenced by the model. Is a model designed to increase users, for instance, beneficial to subscription rates.
Track metrics such as the size of data and update frequency, positions, groups, and forms. Model performance concerns are often attributable to changes in the data and its sources, and these metrics can assist in your investigation.
Track the volume used by processing power or memory models.

How Industry Leaders Leverage ModelOps?

Enterprises drive AI into core processes at scale by focusing on three areas:

Model-Centric Approach - Leaders manage models as first-class business properties, enabled by a model-centric infrastructure that facilitates long-term continuous implementation performance independent of data science workbench and execution platforms.
Operations First Mentality - Leaders recognize that deploying models to operation systems allows them to run 24×7 without delay, using the same operating controls, software, and automation that enable other technologies.
Automation - Leaders completely automate ModelOps processes, from implementation to monitoring and governance, to efficiently handle business model SLAs, eliminate manual execution, and mitigate risk, cost, and model time for business.

How is ModelOps different from MlOps?

Aspect	ModelOps	MLOps
Focus	Operationalization of both AI and decision models.	Exclusively focuses on the operationalization of ML models.
Scope	Covers the entire lifecycle of AI models, including optimization models and decision models.	Focuses on machine learning models only.
Integration	Involves AI and decision models across various departments and processes.	Primarily integrates machine learning models.
Deployment	Manages implementation and model workflow during application creation and deployment lifecycle.	Reduces time to deploy ML models from months to hours.
Tools and Performance Monitoring	Provides transparency in deploying and using AI across the enterprise; monitors model performance in real-time.	Offers tools to monitor ML model performance but lacks comprehensive enterprise-level transparency.
Explainability	Ensures AI-enabled outcomes are explainable and transparent across the enterprise.	Limited explainability focused primarily on ML models.
Organizational Impact	Bridges the gap between teams building and deploying AI, facilitating widespread use of AI in business operations.	Addresses ML model deployment but leaves a gap in end-to-end enterprise integration.

ModelOps Solutions for Enterprises Challenges

Acceleration of AI/ML Activities: Large enterprises have been increasing their AI and machine learning efforts in recent years using ModelOps, which enables the scaling of machine learning models across the enterprise for numerous use cases.
Model Debt Accumulation: Many companies face an increasing amount of "model debt" due to the lack of deployment and refresh of models. This has resulted in missed opportunities, leaving millions of dollars unutilized.
Emergence of AutoML and Citizen Data Scientists: With the rise of AutoML and evolving market trends, citizen data scientists are deploying applications without proper oversight from enterprise IT, creating governance challenges.
Governance and Accountability Issues: The lack of accountability and proper controls in these systems can lead to governance risks and increased costs. Large-scale models require close governance during deployment to ensure regulatory compliance.
ModelOps as a Solution: ModelOps addresses these challenges by acting as a partner for the data science team, enabling better use of technologies and tapping into data repositories across different business units.
Operationalizing Models: ModelOps focuses on operationalizing models generated from historical data and deploying them on production data, ensuring smooth integration of analytics throughout the entire ModelOps lifecycle.
End-to-End ModelOps Lifecycle: ModelOps supports the entire lifecycle of model development and deployment, ensuring all business contexts are covered, improving business outcomes, and reducing model debt.

Conclusion

As more enterprises adopt ModelOps to manage AI and decision models at scale, we can expect a significant reduction in unutilized models, improved decision-making, and enhanced business outcomes. In the future, ModelOps will become an indispensable part of the AI ecosystem, driving AI success across industries.