Everything about ModelOps
Artificial intelligence governance
ModelOps, as defined by Gartner, "is focused primarily on the governance and lifecycle 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" in Multi-Agent Systems. "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 data scientists. Wikipedia →
🧠Start here: a 6-step reading path
- Read the overview: ModelOpsA 5-minute orientation
- Pick up a classic book: Modern Data Mining with PythonThe most-reprinted book on the subject
- Read the landmark paper: ModelOps for enhanced decision-making and governance in emergency control roomsCited 13 times
- Go sideways: MLOpsA closely related topic
- Go sideways: AIOpsA closely related topic
đź“– Definitions
No dictionary entry for this exact phrase.
🗂️ The subject at a glance
🏆 Landmark papers (most cited)
đź“š Books worth reading
- 📕Modern Data Mining with PythonDushyant Singh Sengar, Vikash Chandra · 2024
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
đź”— Related topics
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