The practice notebooks inDocumentation Index
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notebooks/02_practica/ provide structured exercises that go beyond the live demonstration and give students the opportunity to apply MLflow tracking independently. Where the live coding notebooks follow an instructor’s narration, the practice notebooks are designed for self-directed work: students encounter a problem, attempt a solution, check against the reference, and finally tackle an open-ended challenge that mirrors the hardest mission in the Streamlit app. The three notebooks form a deliberate progression — guided exercises, worked solution, and autonomous challenge — matched to the same escalating difficulty as the Tatooine → Coruscant → Mustafar mission arc.
Available notebooks
01_Tracking_Ejercicios.ipynb
Initial tracking exercises. Students practice each MLflow call in isolation and in combination: registering an experiment, opening a run with a context manager, logging a parameters dictionary, logging a metrics dictionary, saving artifact files, and logging the trained sklearn model. Each exercise includes a brief prompt and a blank cell for the student’s solution.
02_Archivos_Jedi_SOLUCION.ipynb
Complete solution reference. Every exercise from
01_Tracking_Ejercicios.ipynb is solved with working, annotated code. Instructors use this to check student work, provide targeted hints, or display on screen when a class is stuck. Students can consult it after a genuine attempt — using it as a first resort defeats the purpose of the exercises.03_Orden_66_RETO.ipynb
The final challenge — Orden 66. Students apply all six MLflow concepts autonomously in a single, unsupported notebook. The challenge mirrors the Mustafar autonomous mission in the app: no scaffolding, no hints, and the full 5–4–2–1 tracking pattern must be produced from memory. Completing it successfully is the clearest evidence that a student has internalised the complete tracking workflow.
Progression
The three notebooks follow the same escalating difficulty arc as the app missions:| Notebook | Equivalent app stage | Support level |
|---|---|---|
01_Tracking_Ejercicios.ipynb | Chambers 1–6 + Tatooine | Guided — prompts provided |
02_Archivos_Jedi_SOLUCION.ipynb | Instructor reference | Full solution visible |
03_Orden_66_RETO.ipynb | Mustafar (autonomous) | None — blank canvas |
Using notebooks alongside the app
The practice notebooks use the same Digits dataset, the same Random Forest algorithm, and the same five-parameter dictionary (dataset, n_estimators, max_depth, test_size, random_state) as the Streamlit app. As a result, the metric values produced in the notebooks are directly comparable to the orientative results shown in the app’s Comparador and Consejo screens. Students who complete both the app missions and the practice notebooks will notice the numbers aligning — reinforcing the idea that MLflow is a reproducible record of a real, deterministic experiment.
The shared parameter schema also means a student can copy the parametros dictionary from a notebook cell directly into the app’s mission editor (or vice versa) without modification.
Running the notebooks
Install the required packages and launch JupyterLab from theapp-v2 directory:
notebooks/02_practica/ relative to the repository root. Navigate there in the JupyterLab file browser after launch.
No additional configuration is needed. MLflow will create a local SQLite tracking database and an mlruns/ artifact folder automatically on the first run, in whichever directory the notebook kernel is started from.