Scorecard Objective
This scorecard helps teams benchmark MLOps maturity and prioritize the highest-risk operational gaps before scaling model deployment.
- Assess readiness across data, model, platform, and governance dimensions.
- Convert maturity gaps into a practical 90-day improvement roadmap.
- Standardize language between product, engineering, and risk teams.
Maturity Dimensions
- Data quality: validation coverage, freshness SLAs, and drift visibility.
- Model lifecycle: versioning, reproducibility, and release discipline.
- Serving reliability: latency budgets, rollback safety, and availability targets.
- Monitoring: performance, bias, and business KPI observability.
- Governance: approvals, audit artifacts, and policy compliance evidence.
Scoring Method
Rate each dimension from 1 to 5 with evidence requirements:
- Level 1: ad-hoc, manual, undocumented.
- Level 3: repeatable patterns with partial automation.
- Level 5: fully instrumented, policy-aligned, and continuously improved.
90-Day Improvement Plan
- Month 1: close data quality and observability blind spots.
- Month 2: standardize deployment rollback and model release checklists.
- Month 3: align governance evidence, ownership, and quarterly review cadence.
FAQ
- How often should maturity be rescored?
Run a full rescore quarterly and a lightweight monthly checkpoint on critical production systems.
- Who should perform the scoring?
Use a cross-functional panel from engineering, data, product, and governance to avoid one-sided assessments.
- What score is good enough to scale?
A consistent level 3+ across reliability, monitoring, and governance is a practical minimum for broader rollout.