Exam Professional Machine Learning Engineer topic 1 question 158 discussion

minimal development work + regression model = BigQuery ML

B. Develop a regression model using BigQuery ML. You're looking for a solution that scales smoothly and requires minimal development work. BigQuery ML is an excellent choice because it allows you to create machine learning models directly in BigQuery, without the need to write code or set up complex infrastructure.

Scalability: BigQuery is a serverless data warehouse designed to handle massive datasets. It can efficiently process tens of millions of records daily for model training. Minimal Development Work: BigQuery ML offers built-in regression models like linear regression that you can train directly on your data stored in BigQuery. This eliminates the need for extensive custom code development with TensorFlow, PyTorch, or scikit-learn (options A, C, and D). Daily Training Runs: BigQuery ML allows scheduling queries for automated model training. You can set up a daily scheduled query to train your model on the latest data.

Minimal development effort can be achieved with BigQuery ML. Also the amount of data is already in BQ.

Minimal dev effort => BigQueryML

I went C.

Minimal development effort => BigQueryML

for scheduling daily training runs with minimal development work and seamless scaling, the best option is to develop a regression model using BigQuery ML (Option B). It allows you to perform model training and inference directly within BigQuery, taking advantage of its distributed processing capabilities to handle large datasets effortlessly.