Google Vertex AI is Google Cloud's unified, fully managed MLOps platform for building, deploying, and managing machine learning and generative AI workloads at any scale. It covers the complete lifecycle — from interactive notebook development and dataset management through custom training, AutoML, and model serving to feature stores, pipeline orchestration, and production monitoring. The key mental model is that Vertex AI is not a single tool but a tightly integrated suite: nearly every resource (datasets, models, endpoints, experiments) lives in a Model Registry or dedicated store that pipelines, notebooks, and monitoring jobs reference by ID, making end-to-end reproducibility a first-class concern rather than an afterthought.
What This Cheat Sheet Covers
This topic spans 15 focused tables and 96 indexed concepts. Below is a complete table-by-table outline of this topic, spanning foundational concepts through advanced details.
Table 1: Notebook Environments
Vertex AI offers two distinct interactive development environments, each targeting different use-case patterns and team needs — choosing the right one early prevents painful migrations later.
| Type | Example | Description |
|---|---|---|
gcloud workbench instances create my-instance --machine-type=n1-standard-4 --location=us-central1-a | • JupyterLab on a Compute Engine VM • supports GPUs, custom conda environments, GitHub sync, and idle shutdown | |
Share notebook → set IAM → collaborate in real-time in browser | • Managed, serverless notebook environment integrated with Vertex AI and BigQuery • emphasizes collaboration and Gemini AI-assisted coding | |
--idle-shutdown-timeout=10800 (3 h) | • Automatically stops (not terminates) the VM after the configured period of kernel inactivity • preserves disk but halts CPU/GPU billing |