start with the idea before the implementation.
the mechanisms you need to reason about.
Run metadata
Run metadata is studied through intuition implementation evidence and trade-offs. The goal is to be able to explain the mechanism and verify it with a concrete test rather than only repeat a definition.
Parameters
Parameters is studied through intuition implementation evidence and trade-offs. The goal is to be able to explain the mechanism and verify it with a concrete test rather than only repeat a definition.
Metrics
Metrics is studied through intuition implementation evidence and trade-offs. The goal is to be able to explain the mechanism and verify it with a concrete test rather than only repeat a definition.
Artifacts
Artifacts is studied through intuition implementation evidence and trade-offs. The goal is to be able to explain the mechanism and verify it with a concrete test rather than only repeat a definition.
Lineage
Lineage is studied through intuition implementation evidence and trade-offs. The goal is to be able to explain the mechanism and verify it with a concrete test rather than only repeat a definition.
turn the lesson into evidence.
define a run schema
Build the smallest version first. Record the input, expected output, measured result and one failure you discovered.
track two models
Build the smallest version first. Record the input, expected output, measured result and one failure you discovered.
reproduce the winner
Build the smallest version first. Record the input, expected output, measured result and one failure you discovered.
prove you can explain and decide.
capture dataset version
ask cortex to test me →record random seeds
ask cortex to test me →compare runs consistently
ask cortex to test me →what usually goes wrong.
manual notes only
Detect this early by defining a baseline, a measurable signal and a condition that would cause you to stop or redesign the approach.
missing data lineage
Detect this early by defining a baseline, a measurable signal and a condition that would cause you to stop or redesign the approach.
unversioned preprocessing
Detect this early by defining a baseline, a measurable signal and a condition that would cause you to stop or redesign the approach.
Create a short Experiment tracking engineering note with one working artifact one metric one failure case and one decision about when you would or would not use it.
Save the result in your portfolio or project repository. A strong learning artifact should make your assumptions, metrics and failure analysis visible.