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Write a model evaluation plan someone else could follow.
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ML 101. Your first reproducible ML experiment. Write a model evaluation plan someone else could follow. Make the experiment inspectable. Record the prediction task, label policy, data version, split method and baseline before comparing models. Keep a record of which model and settings produced each result. Someone reading the experiment should be able to tell which examples were used for fitting and which were reserved for evaluation. A random seed can help reproduce some operations, but it is not a guarantee that every hardware and software environment will produce identical results. Evaluate the deployed workflow. A model that looks useful offline still needs evaluation on the target device and in the surrounding application. Measure latency, memory and the behavior of uncertain or invalid inputs. Preserve an option to abstain or ask for review when the task needs it. If a new version performs worse, the team should know which version is running and have a defined recovery path. Monitoring is part of operating the system, not a replacement for a sound initial test. State the evidence boundary. Finish with a claim no broader than the experiment supports. “On the held-out sessions from this dataset, this model found eight of ten review cases” is more informative than “the model is reliable.” Include the small sample size and conditions when interpreting the result. A research demonstration, a benchmark and a validated production workflow are different evidence states. Your goal is to make the next decision clearer, including when the evidence says not to deploy yet. HerbDev example. The case study distinguishes implemented research workflows from physical validation that has not yet passed. Use that discipline in your own report: keep what was measured separate from what remains to be tested. The process: Version data and settings. Then Compare a baseline. Then Test on held-out inputs. Then Document limits and monitor. Try this. Write a six-line experiment card: task, data, split, baseline, metrics and deployment check. Pause the audio to try the exercise. A worked answer and checkpoint questions are available on the lesson page.
Make the experiment inspectable
Record the prediction task, label policy, data version, split method and baseline before comparing models. Keep a record of which model and settings produced each result. Someone reading the experiment should be able to tell which examples were used for fitting and which were reserved for evaluation. A random seed can help reproduce some operations, but it is not a guarantee that every hardware and software environment will produce identical results.
Evaluate the deployed workflow
A model that looks useful offline still needs evaluation on the target device and in the surrounding application. Measure latency, memory and the behavior of uncertain or invalid inputs. Preserve an option to abstain or ask for review when the task needs it. If a new version performs worse, the team should know which version is running and have a defined recovery path. Monitoring is part of operating the system, not a replacement for a sound initial test.
State the evidence boundary
Finish with a claim no broader than the experiment supports. “On the held-out sessions from this dataset, this model found eight of ten review cases” is more informative than “the model is reliable.” Include the small sample size and conditions when interpreting the result. A research demonstration, a benchmark and a validated production workflow are different evidence states. Your goal is to make the next decision clearer, including when the evidence says not to deploy yet.
- Version data and settings
- Compare a baseline
- Test on held-out inputs
- Document limits and monitor
Try it yourself
Write a six-line experiment card: task, data, split, baseline, metrics and deployment check.
Compare with a worked answer
Task: flag photos for review. Data: versioned, consistently labeled captures. Split: separate capture sessions. Baseline: always routine. Metrics: recall, precision and error counts. Deployment check: latency and review behavior on the intended phone, including low light and offline use. Report the limits instead of claiming general validation.
Check your understanding
Choose one answer for each question, then check your score. Read the explanations and retry any question you missed.
Read the answer guide
1. Data version, splits, model settings and evaluation procedure Reproduction requires knowing what was trained, on which examples, with which settings and how it was evaluated.
2. No A seed helps control some randomness, but software, hardware and nondeterministic operations can still affect results.
3. Report the measured outcome and the tested conditions A useful conclusion states what the evidence supports and what remains untested.
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Apply it in the final project · Choose what to learn next →Keep exploring: What next? · AI vocabulary · AI 101 · Learning hub