adapt-model-training

# Adapt Model Training Target file: `notebooks/2_model_training_and_deployment/model_training.ipynb` Target job: `resources/2_1_model_training_job.yml` ## When to use Use this skill whenever the user wants to: - Swap `DecisionTreeClassifier` for a different algorithm. - Change the problem type (regression, clustering, forecasting, …). - Change features, target column, or evaluation metric. - Rename the registered model in Unity Catalog. ## Step-by-step 1. **Pick the algorithm for the problem type.** - **Regression**: `LinearRegression`, `XGBRegressor`, `LGBMRegressor`, `RandomForestRegressor`, etc. - **Classification**: `RandomForestClassifier`, `LogisticRegression`, `XGBClassifier`, etc. - **Clustering**: `KMeans`, `DBSCAN`, etc. — note: no target column. - **Forecasting**: Prophet, ARIMA, or DL approaches. 2. **Update `features` and `target`.** Match the columns produced by the adapted data ingestion notebook. For clustering, drop the `target` variable and any train/test split that depends on it. 3. **Update logged metrics.** Choose metrics appropriate to the problem type: - Regression → `rmse`, `mae`, `r2`. - Classification → `accuracy`, `f1`, `precision`, `recall`, `roc_auc`. - Clustering → `silhouette_score`, `davies_bouldin_score`. 4. **Update best-run selection.** Change `selected_metric` to the metric you actually optimize, and flip the direction (`maximize` vs `minimize`) accordingly. 5. **Rename the registered model.** Update `model_name` (currently `iris_model`) and keep the three-level reference `{catalog_name}.{schema_name}.<model_name>`. 6. **Keep the Challenger alias.** Register every new training run with the `@challenger` alias. The deployment pipeline will promote it to `@champion` later. Do not write `@champion` from the training notebook. 7. **Let the signature stay inferred.** `mlflow.models.infer_signature` is driven by the `X_train` / `y_train` you pass in — just make sure they are representative. 8. **Sync the training job YAML.** Update `resources/2_1_model_training_job.yml`: - Task `notebook_path` if you rename the notebook. - `base_parameters` if you add new widgets. - `email_notifications.on_failure` to the team's address. ## Parameterization contract Keep widgets for `catalog_name` and `schema_name`. The MLflow experiment should remain user-scoped — `/{user}/{model}_{catalog}` — to avoid collisions when multiple developers train in parallel. See `mlops-quickstart-overview` for the full contract. ## Edge cases - **Clustering**: remove evaluation steps that depend on a target column (e.g. accuracy). Use unsupervised metrics like silhouette score. - **Imbalanced classification**: log `f1_weighted` or `roc_auc` instead of raw accuracy; consider class weights or resampling. - **Forecasting with seasonality**: log the model artifact plus a holdout forecast plot. Track both point metrics (RMSE) and percentile errors (MAPE, sMAPE). - **Hyperparameter search**: wrap runs inside a parent MLflow run and rely on the existing best-run selection logic to pick the registered version. - **Deep learning**: log the model with `mlflow.pyfunc` or framework-native flavor (`mlflow.pytorch`, `mlflow.tensorflow`) — the rest of the pipeline is flavor-agnostic.