adapt-model-training

name	adapt-model-training
description	Adapt the MLOps Quickstart model training notebook from the Iris classifier placeholder to a custom algorithm and problem type (regression, clustering, classification, forecasting). Use when the user wants to change the model, features, target, metrics, or registered model name, or when they ask how to fit a different ML problem into this template.

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.