ModelOps: Maximizing the Value of Machine Learning and Analytics

Developing a ModelOps Process

Businesses must implement a process of ModelOps best practices that defines a set of criteria for evaluating the cycle and performance of ML models. For example, ModelOps aids in development, deployment, and testing of ML models so that businesses can verify the effectiveness and quality of their models for real-world use cases. 

Not to be confused with MLOPs, which targets the operationalization of ML models, ModelOps is used to target the operationalization of all analytics, AI and decision models. MLOps is also considered to be a factor in data sciences platforms vs. ModelOps, which is an enterprise capability.

Organizations need to build, manage, and measure their models and be prepared to continuously update and refine those models over time, including providing application functionality in the cloud. This requires the capabilities to automate data preparation, provide a level of explainability into their models, and evaluate possible bias. ModelOps can even aid in the development and deployment of more complex ML models. 

That’s not all. Businesses also need to maintain all of their models with the capabilities for teams to track model progress, and ModelOps aids businesses in establishing a framework to enhance collaboration between teams and departments. That way people can continuously update, evaluate, and control models while bringing transparency into AI and analytics. 

ModelOps Benefits

ModelOps enables reliable outcomes for analytics, AI and decision models so that businesses can make the best possible decisions once the models are running in production. ModelOps intersects with DevOps and MLops, bringing models from creation to production while providing continuous updates and retraining. 

Another advantage of ModelOps is that it can support a more holistic approach to analytics model development. Models aren’t stagnant and are impacted by dynamic environments and the populations they’re intended to represent. They’re also impacted by continuous relationship changes, such as concept drift, data drift, and business drift. For example, data drift can impact the accuracy of a model’s predictions and is one of the main contributors to model degradation over time. But monitoring, evaluating and testing model behaviors and efficacy via ModelOps can ensure the viability and quality of the model’s performance.

Here’s a quick look at some other benefits of ModelOps:

• End-to-end visibility and auditability into model production
• Enhances and automates governance for AI and analytics models
• Automates the model lifecycle
• Opens up model deployment to business analysts 
• Ensures model accountability and repeatability 
• Accelerates AI project outcomes and time to value, and streamlines model deployment

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A New Outlook for AI Operationalization

Managing models demands significant oversight - from the data and predictions to governance and model accuracy. All of these factors can put additional pressures on data science resources. ModelOps can alleviate many of the challenges associated with operationaling models and empower organizations to accelerate their enterprise AI strategies while maximizing their analytics investments.

If you’re interested in learning more about ModelOps, check out Simplilearn's AI and ML Course course for additional resources and educational information.