Build the first working model-run dictionary

Build only what the claim requires: required keys name/accuracy/ready/owner exist and optional version falls back safely. Prefer boring, deterministic code over frameworks you cannot yet explain. Run the path twice; identical output on this fixture is a feature, not a lack of creativity.

run={'name':'ready-v1','accuracy':0.91,'ready':True,'owner':'ml-lab'}
run['version']=run.get('version','unversioned')
print(run['name'], run['accuracy'], run['version'])

Expected evidence: ready-v1 0.91 unversioned. Read each printed intermediate as part of the argument that the path works—not as decoration.

Narrate the journey from raw input to result for a single example from name='ready-v1', accuracy=0.91, ready=True, owner='ml-lab'. If you cannot name an intermediate, the implementation is still too opaque for this lab. Only after this path is solid should you generalize data sources or UI.

While implementing the model-run dictionary, print or log at least three intermediates that map to the claim (required keys name/accuracy/ready/owner exist and optional version falls back safely). Good intermediates are values a teammate could recompute with a calculator or diff. Bad intermediates are framework traces you cannot explain.

Re-run with name='ready-v1', accuracy=0.91, ready=True, owner='ml-lab' twice. If the second run differs, either the path is nondeterministic (document the seed) or you have hidden global state—both are lab bugs until named.

Stop adding features once the path supports store one training run so later code can read named fields without positional guessing. Extra UI, extra tools, or extra models belong in later chapters. The mastery bar for this page is simply: a deterministic end-to-end path with intermediates.

At the implementation stage for python-dictionaries, the job is narrower than finishing a product demo. You are creating one progressive evidence piece about name='ready-v1', accuracy=0.91, ready=True, owner='ml-lab' that later pages inherit without redefining success. Keep that fixture small enough to inspect by hand, keep outputs copy-pasteable as text, and refuse to narrate this baseline as if it were a production SLA: a handwritten field list before coding the dict.

For this page specifically, success looks like a deterministic path with printed intermediates while still centering the user decision to store one training run so later code can read named fields without positional guessing. If you cannot point to a file, command, or assertion that proves that for the model-run dictionary, stay on this page instead of advancing.

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