ML Waste Risk Scanner

Answer 10 questions and get a risk score. Designed for enterprise ML platforms.

Assessment

This assessment does not collect data. It runs locally in your browser. Use it to prioritize the next steps.

1) Do you track GPU utilization per run? 2) Do you automatically detect duplicate training runs? 3) Do you stop OOM loops / retry storms automatically? 4) Do you enforce artifact output for production pipelines? 5) Do you know cost per pipeline (baseline + variance)? 6) Are GPUs frequently idle due to data pipeline bottlenecks? 7) Do you frequently re-run similar experiments without a clear reason? 8) Do you have warn/stop guardrails for high-confidence waste patterns? 9) Are endpoints / GPU services left running after tests? 10) Can Finance explain ML spend drivers in one page?

Interpretation

Low Risk

You likely have basic governance. Focus on continuous improvement and verification.

Medium Risk

Some waste patterns are likely present. A short audit can uncover quick savings.

High Risk

Expect significant hidden waste. Start with verified savings and guardrails.

Next Step

Want a finance-grade breakdown of waste and savings opportunities? Request a free ML cost audit.

Request Free ML Cost Audit