New ModelOp Center Capabilities Improve Governance, Management and Monitoring of AI and ML Models

ModelOp Center unlocks the value of AI and ML investments, reducing model operationalization costs by as much as 30% and accelerating time to operationalization by 50%

ModelOp, the pioneer of ModelOps software for large enterprises, announced several upgrades to its ModelOp Center software that improves how artificial intelligence (AI) and machine learning (ML) models are governed, managed and monitored, enabling customers to make smarter decisions based on reliable, compliant and scalable AI initiatives.

AI and ML adoption is on the rise as more organizations turn to technology to improve customer service, detect fraud, and advance their digital businesses. However, many of these organizations struggle to operationalize AI and ML models after they’re developed. According to Gartner, more than 50 percent of AI models never go into production fully due to insufficient model operational processes.

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ModelOp Center addresses this challenge by automating the governance, management and monitoring of post-development AI/ML models across platforms and teams, accelerating time to production, and reducing costs and business risks.

“AI and ML technologies are making tremendous contributions to industries like banking, finance, insurance, healthcare and manufacturing, but only if they are actually used for production business decisioning and working properly,” said Dave Trier, ModelOp vice president of product. “AI and ML models require oversight to ensure they’re producing the most accurate information, while remaining in compliance with regulatory, business, and technical constraints. ModelOp Center provides the governance, management and monitoring needed to unlock the value of AI and ML investments, ensuring they scale and perform as expected.”

New capabilities included in the ModelOp Center upgrade include:

• Associated models, which enables organizations to associate custom monitors, controls or pre/post-processing in a consistent way across groups of models. Models are often used in the model life cycle for repeatable activities such as drift testing, back testing or enforcing thresholds. Associated models enable standardization for these common activities and eliminate the need for data scientists to create redundant models for these processes.
• Custom metadata that is gathered from model risk management (MRM), compliance, business, IT, and other systems and maintained with every model and used throughout the model’s life cycle for process decisioning and controls.
• ServiceNow upgraded integration, which makes it possible for customers to incorporate incident and change management in the model life cycle and automatically generate, route and track incident tickets and change requests for ongoing management and auditability.

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ModelOp Center provides extensive and stringent governance of operational models, maintaining a production model inventory and enforcing regulatory, compliance and business controls. A comprehensive process library, custom metadata and associated models accelerate time to operationalization by as much as 50 percent.  Real-time monitoring and integration with development platforms, IT systems, Model Risk Management systems and business applications helps customers automate and scale AI and ML models, reducing costs by up to 30 percent.

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