Evaluation scorecard
Metrics, slices, and acceptance rules
A compact rubric to compare models fairly and avoid moving the goalposts after training.
Templates and checklists
Reusable resources for applied ML
These resources are designed to be copied into your docs or tickets. They help teams align on success metrics, catch common data issues early, and ship models with monitoring and documentation in place. The downloads on this page are lightweight previews; for tailored versions that match your domain and constraints, request a consult.
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Download previews
Use these as starting points for internal reviews. Each preview includes a short “how to use” section and the minimum fields needed to make decisions. If you are advertising an AI-powered product, the templates also include spaces for clear limitation statements so your claims remain accurate.
Data readiness checklist
Leakage and quality checks
A short checklist to catch the issues that invalidate experiments.
Model card outline
Purpose, limitations, monitoring
A lightweight documentation structure to keep claims accurate and traceable.
Monitoring plan template
Signals you can act on
Defines what to monitor, how often, who owns it, and what happens when alarms trigger.
How to use these templates
Start with the evaluation scorecard before training. It forces a clear definition of “good enough” and highlights what must be tested across segments. Then run the data readiness checklist to validate splits, labels, and feature availability. Once you have results, capture them in the model card so future updates remain traceable. Finally, use the monitoring plan to define signals and owners before the first production rollout.
If you are unsure what to include, our guides explain each field in context. For tailored support, we can review your existing artifacts and propose improvements that match your risk profile.
✨ Practical note
Keep templates short. If a field does not influence a decision, remove it. The goal is fewer surprises, not more paperwork.
Good default
One page for evaluation, one page for monitoring, and a concise model card.