“AI model operationalization (ModelOps) is primarily focused on the governance and life cycle management of all AI and decision models (including models based on machine learning, knowledge graphs, rules, optimization, linguistics and agents). In contrast to MLOps, which focuses only on the operationalization of ML models, and AIOps which is AI for IT Operations, ModelOps focuses on the operationalization of all AI and decision models.”
Gartner, Innovation Insight for ModelOps, August 2020
Farhan Choudhary, Shubhangi Vashisth, Arun Chandrasekaran, Erick Brethenoux
ModelOps includes MLOps
“ModelOps refers to the operationalization of all AI models and includes MLOps that deals with the operationalization of ML models”
Gartner, “Use Gartner’s 3-Stage MLOps Framework to Successfully Operationalize Machine Learning Projects”, July 2, 2020, Shubhangi Vashisth, Erick Brethenoux, Farhan Choudhary, Jim Hare
This white paper explains what ModelOps is, and how it relates with MLOps, as a response to the growing need of clarity from organizations that are scaling, automating and governing Enterprise AI initiatives.
Bring Enterprise AI Initiatives into production with ModelOps
Leading Analyst view on the ModelOps and MLOps space
Executive practitioners’ sharing their experiences on how to operationalize all models across the enterprise in a world with models of all types, regulations and the need for explainability and visibility.
Lessons learned, what has worked, what hasn’t, and how ModelOps (including MLOps) is a key capability for enterprise AI to adapt and thrive in the new normal we are in.
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