Favorita: XGBoost on Vertex AI
The XGBoost model itself: train, tune with Optuna, and predict — written to run as a Vertex AI job. The interpretable tabular baseline, packaged for managed training instead of a notebook.
Primary Outcome
A tuned XGBoost forecasting model that trains and predicts as a managed Vertex AI job.
Solution
Train, Optuna-tune, and predict scripts that run the XGBoost model as a tracked, reproducible Vertex AI job.
Deliverables
train_xgboost.py— training entry point with feature handlingoptimize_xgboost.py— Optuna hyperparameter searchpredict_xgboost.py— batch prediction with feature importancesIntegration with the Vertex job submission and MLflow tracking layers
Strategic Context
For tabular, store-item forecasting, XGBoost remains hard to beat on the accuracy-per-effort curve. The value here isn't the algorithm — it's having it tuned, tracked, and running on managed infrastructure so results are reproducible rather than artisanal.
Technical Architecture
For tabular, store-item forecasting, XGBoost remains hard to beat on the accuracy-per-effort curve. The value here isn't the algorithm — it's having it tuned, tracked, and running on managed infrastructure so results are reproducible rather than artisanal.
Problem Statement
XGBoost forecasting models stay stuck in notebooks with hand-tuned parameters, so results can't be reproduced or run on managed infrastructure.
Links
What's Included
The vertex/models/xgboost directory: train_xgboost.py, optimize_xgboost.py, and predict_xgboost.py, designed to run via the Vertex job submission layer and log to MLflow.
FAQs
Why Optuna?
It searches the hyperparameter space far more efficiently than grid search, which matters when each trial runs on managed infra.
Can I run it without Vertex?
The scripts are standard Python — you can run them locally and skip the submission layer for development.
Tech Stack
Tool 1
Tool 4
Tool 4
Tool 3
Tool 2
Tool 4
Primary Outcome
A tuned XGBoost forecasting model that trains and predicts as a managed Vertex AI job.
Problem Statement
XGBoost forecasting models stay stuck in notebooks with hand-tuned parameters, so results can't be reproduced or run on managed infrastructure.
Solution
Train, Optuna-tune, and predict scripts that run the XGBoost model as a tracked, reproducible Vertex AI job.
Links
Deliverables
train_xgboost.py— training entry point with feature handlingoptimize_xgboost.py— Optuna hyperparameter searchpredict_xgboost.py— batch prediction with feature importancesIntegration with the Vertex job submission and MLflow tracking layers
What's Included
The vertex/models/xgboost directory: train_xgboost.py, optimize_xgboost.py, and predict_xgboost.py, designed to run via the Vertex job submission layer and log to MLflow.
Strategic Context
For tabular, store-item forecasting, XGBoost remains hard to beat on the accuracy-per-effort curve. The value here isn't the algorithm — it's having it tuned, tracked, and running on managed infrastructure so results are reproducible rather than artisanal.
FAQs
Why Optuna?
It searches the hyperparameter space far more efficiently than grid search, which matters when each trial runs on managed infra.
Can I run it without Vertex?
The scripts are standard Python — you can run them locally and skip the submission layer for development.
Technical Architecture
For tabular, store-item forecasting, XGBoost remains hard to beat on the accuracy-per-effort curve. The value here isn't the algorithm — it's having it tuned, tracked, and running on managed infrastructure so results are reproducible rather than artisanal.
Tech Stack
Tool 1
Tool 4
Tool 4
Tool 3
Tool 2
Tool 4