MLOps Maturity Scorecard

Turn an ML stack description into an evidence-bound maturity report

MLOps maturity discussions become vague when named tools are mistaken for working controls. This scorecard assesses the processes the team actually describes and keeps unknown dimensions unknown.

What it produces

• Scores across versioning, pipeline automation, model CI/CD, monitoring and drift, serving fit, cost control, incident response, and governance
• Verified, Inferred, or Assumed evidence labels for every scored finding
• A deterministic weighted total with a coverage calculation
• An Insufficient coverage result when less than 60% of the rubric is supported
• Three prioritized gaps with one bounded first step each
• A routing block to deeper serving, gate, drift, or failure analyses when relevant

Best for

• ML teams preparing an internal improvement roadmap
• Platform leads locating lifecycle gaps before selecting tools
• Teams that need a repeatable baseline without pretending missing evidence exists
• Reviewers comparing stated practice with an explicit rubric

Boundaries

This is a structured self-assessment, not an external audit, certification, compliance opinion, or industry benchmark. It does not inspect infrastructure, access accounts, diagnose a specific incident, or prove that a control works.