AI ModelOPS

ModelOps (model operations), as defined by Gartner, "is focused primarily on the governance and life cycle management of a wide range of operationalized artificial intelligence (AI) and decision models, including machine learning, knowledge graphs, rules, optimization, linguistic and agent-based models". “ModelOps lies at the heart of any enterprise AI strategy”. It orchestrates the model life cycles of all models in production across the entire enterprise, from putting a model into production, then evaluating and updating the resulting application according to a set of governance rules, including both technical and business KPI's. It grants business domain experts the capability to evaluate AI models in production, independent of data scientists. Illustrates the ModelOps cycle

As noted in a Forbes article on the essential role ModelOps plays in the delivery of enterprise AI solutions: "As enterprises scale up their AI initiatives to become a true Enterprise AI organization, having full operationalized analytics capability puts ModelOps in the center, connecting both DataOps and DevOps."

ModelOps (or AI model operationalization) is focused primarily on the governance and life cycle management of a wide range of operationalized artificial intelligence (AI) and decision models, including machine learning, knowledge graphs, rules, optimization, linguistic and agent-based models. Core capabilities include continuous integration/continuous delivery (CI/CD) integration, model development environments, champion-challenger testing, model versioning, model store and rollback.

Artificial Intelligence Model Operationalization (ModelOps)

Artificial intelligence (AI) model operationalization (ModelOps) is a set of capabilities that primarily focuses on the governance and the full life cycle management of all AI and decision models. This includes models based on machine learning (ML), knowledge graphs, rules, optimization, natural language techniques 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 operationalizing all AI and decision models.

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