ModelOps

<p>ModelOps (model <a href="/facts/IT_operations/T4XmWP6n">operations</a> or model operationalization), as defined by <a href="/facts/Gartner/9hd9zakC">Gartner</a>, "is focused primarily on the <a href="/facts/Data_governance/nt0J5rgc">governance</a> and <a href="/facts/Application_lifecycle_management/J9fDtyl7">lifecycle management</a> of a wide range of operationalized <a href="/facts/Artificial_intelligence/lJGauQwX">artificial intelligence</a> (AI) and <a href="/facts/Decision_model/xB3mM2kh">decision models</a>, including <a href="/facts/Machine_learning/e0w0XJTu">machine learning</a>, <a href="/facts/Knowledge_graph/0GrfBIqx">knowledge graphs</a>, rules, optimization, linguistic and <a href="/facts/Agent-based_model/C3C1vR8s">agent-based models</a>" in <a href="/facts/Multi-agent_system/A85vt3UA">Multi-Agent Systems</a>. "ModelOps lies at the heart of any enterprise AI strategy". It orchestrates the model lifecycles 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 key performance indicators (KPI's). It grants business domain experts the capability to evaluate AI models in production, independent of <a href="/facts/Data_scientist/AHYUo0Rn">data scientists</a>.</p>
<p>A <a href="/facts/Forbes/lyULpGhW">Forbes</a> article promoted ModelOps: "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 <a href="/facts/DataOps/T3ysa4NG">DataOps</a> and <a href="/facts/DevOps/yRg6dIBc">DevOps</a>."
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ModelOps open-in-new

ModelOps