Exam Professional Machine Learning Engineer topic 1 question 207 discussion

"You want to predict how many beds will be needed for patients each day in advance based on the scheduled surgeries."

A. BigQuery ML regression: While BigQuery ML offers quick model building, a regression model might not capture the time-series aspect of daily bed occupancy. Daily bed occupancy might have trends or seasonality which a plain regression model wouldn't capture. B. BigQuery ML ARIMA: ARIMA models are specifically for stationary time series data, and hospital bed occupancy might not always be stationary (e.g., holiday season might lead to higher occupancy). Additionally, ARIMA models typically don't incorporate additional features like the number of scheduled surgeries. C. Vertex AI AutoML Regression: Similar to option A, a regression model might not capture the time series aspect. While Vertex AI offers AutoML regression, using a solution designed for time series forecasting is more suitable here.

not b: ARIMA does not use number of scheduled surgeries, and it is stated that the prediction must be based on that variable

https://cloud.google.com/vertex-ai/docs/tabular-data/forecasting/overview

D* not C Forecasting models are well-suited for predicting future values based on past trends, making them ideal for the goal of predicting bed occupancy for upcoming days. Dataset is too small for bigquery.

While ARIMA models are commonly used for time series forecasting, they are more suitable for univariate time series data and might require additional manual intervention for feature engineering. In this case, we have multiple variables such as the number of scheduled surgeries, the number of beds occupied, and the date. AutoML Forecasting in option D is designed to handle multivariate time series data, and it automates much of the modeling process, including feature selection and hyperparameter tuning. This can potentially result in a faster and more efficient development and testing process compared to manually implementing and tuning an ARIMA model.