Analyzing the Usability, Performance, and Cost-Efficiency of Deploying ML Models on BigQuery ML and Vertex AI in Google Cloud
Year: 2024
Type: conference-paper
Abstract: This study compared and analyzed the usability, performance, and cost-efficiency of deploying Machine Learning (ML) models in two ML-AI platforms in Google Cloud: BigQuery ML and Vertex AI. Through the experiments with two separate cases, the analysis was conducted with MIMIC-IV datasets of hospitalized patients to deploy regression models on each platform to predict mortality and progression of diseases. The documentation, learning curve, and resource suitability of the platforms were evaluated to access their usability. The study evaluated the total running times and resource utilizations, including storage and compute, to analyze their performance and cost efficiency. The analysis results showed that BigQuery ML offers good usability with easy-to-follow documentation and a moderate learning curve for cloud users, making it more suitable for SQL-savvy users and large-scale data analytics tasks. It also showed efficient resource management and deployment despite its higher initial processing times during the training. Vertex AI incurred higher costs due to longer training times and specific resource allocations. The findings indicate that BigQuery ML seems to be more efficient, particularly in terms of processing time and cost for the experimented clinical dataset and regression models, emphasizing its suitability for large-scale data processing tasks where efficiency is essential.
Authors: Hongyu Wang, Jeong Yang, Gongbo Liang, Young Lee, Zechun Cao
Institution: Texas A&M University – San Antonio
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4404172411