Exam Professional Machine Learning Engineer topic 1 question 276 discussion

Minimal Coding: BigQuery ML provides a user-friendly interface for training models, minimizing the need for extensive coding in tools like TensorFlow (C & D) Efficient Data Processing: Training directly in BigQuery eliminates data movement and leverages BigQuery's scalable infrastructure.

least amount of coding--> BQML recommendations--> matrix factorization

Using BigQuery ML for training a matrix factorization model would require less coding compared to building a custom model with TensorFlow in a Vertex AI Workbench notebook. BigQuery ML provides high-level APIs for machine learning tasks directly within the BigQuery environment, thus reducing the amount of coding needed for data preprocessing and model training. Matrix factorization is a commonly used technique for recommendation systems, making it a suitable choice for recommending new games to users based on their gameplay time data, user metadata, and game metadata.

B https://developers.google.com/machine-learning/recommendation/collaborative/matrix