ModelOps is the discipline of defining, implementing, monitoring, automating, and improving the life cycle of analytical models, including but not limited to artificial intelligence (AL) and machine learning (ML) models, including Large Language models (LLM). For the purposes of this study, we define models to include all types of artificial intelligence and machine learning models as well as less-sophisticated analytical and decision-intelligence models.
Our second annual ModelOps Market Study provides actionable clarity on optimizing the entire analytical model lifecycle. As dependency on predictive models intensifies, effective governance and management become imperative. Our findings reveal the varying maturity of ModelOps capabilities across regions, industries, and organizations. For many, significant gaps persist between goals and realities. This report aims to empower data leaders across sectors with evidence-based insights for maximizing value from analytical models in a secure, scalable, and ethical manner.
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