Checklist

Industrial AI Pilot Evaluation Checklist (MLOps-lite)

An actionable, saveable checklist to evaluate an industrial AI/ML pilot against operational criteria: performance, data drift, explainability, rollback and mitigation, human-in-the-loop rules, latency constraints, and ownership. Captures evidence, recommendation, and reviewer decisions for governance and audits.

Interactive Tool

Industrial AI Pilot Evaluation Checklist (MLOps-lite)

Purpose: A short, operational checklist to evaluate whether an industrial AI pilot is ready to scale or needs further controls. Use this during pilot reviews to ensure teams consider safety, reliability, operator workflows, monitoring, rollback, explainability, and ownership—not just accuracy numbers.

How to use: Complete required fields, attach links to validation artifacts, and record the reviewer, outcome, and recommended actions. Saved responses create an audit trail for governance, audits, and post-mortems.

Baseline metric(s) and current value(s)

E.g., OEE=72%, false alarm rate=3%, mean time between failures (MTBF). List metric name and baseline numeric value.

Is the validation/test set independent of training data?

Confirm there is no data leakage or label reuse between training and validation/testing.

Expected uplift vs. cost and acceptance criteria

Describe expected measurable improvement, minimum acceptable uplift, anticipated savings or cost impact, and the acceptance criteria for scaling.

Monitoring plan for performance and data drift

List metrics to monitor, sampling frequency, drift detection methods, alert thresholds, and who receives alerts.

Rollback and mitigation plan

Describe the steps to safely revert to the previous behavior, how to validate rollback success, and fallback operator actions.

Human-in-the-loop rules and operator responsibilities

Define when operators must intervene, decision authority, override procedures, and training required for responders.

Latency & throughput constraints and test evidence

Specify acceptable response times, throughput limits, test results, and evidence that real workloads meet these constraints.

Ownership and maintenance responsibilities

Identify the owner for model updates, data stewardship, monitoring, incident response, and governance artifact custody.

Explainability / interpretability evidence

Summarize what explanations are available to operators (feature importances, rule lists, example cases) and any limitations.

Estimated safety/compliance risk level if model fails

Links to evidence and artifacts (validation reports, dashboards, test cases)

Provide URLs, document IDs, or storage locations for replay tests, validation results, and dashboards.

Reviewer confidence in readiness (1 = low, 5 = high)

Rate how confident you are that this pilot can be safely scaled with the specified controls.

Recommendation

Required follow-up actions and owners

If not green, list required remediation items, owners, and target dates.

Reviewer name

Review date

YYYY-MM-DD

Additional notes

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Industrial AI Pilot Evaluation Checklist (MLOps-lite)

Industrial AI Pilot Evaluation Checklist (MLOps-lite)

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