Google Cloud Professional Data Engineer Exam 2026 - Free Practice Questions and Study Guide

Deploying machine learning models in Google Cloud can be effectively accomplished through AI Platform, which is specifically designed for this purpose. AI Platform is a managed service that allows you to train and serve machine learning models at scale. It provides an end-to-end solution for the entire machine learning lifecycle, including data preparation, model training, and deployment.

Using AI Platform comes with several advantages, such as support for various machine learning frameworks (like TensorFlow and scikit-learn), the ability to handle model versioning, and options for auto-scaling based on traffic. This tailored service ensures that models can reliably serve predictions while also allowing for seamless updates and management.

Alternative methods may involve using Cloud Functions, App Engine, or Cloud VMs, but those approaches lack the dedicated features that AI Platform provides. For instance, while Cloud Functions enables event-driven execution of code, it is not optimized for the continuous serving of machine learning models. App Engine is more suited for web applications, and Cloud VMs can offer flexibility but require more manual configuration and management compared to the streamlined capabilities of AI Platform. Thus, leveraging AI Platform for model deployment is the most efficient and effective choice in Google Cloud.