Vertex AI Workbench with Terraform: Your ML Workspace on GCP 🔬

Vertex AI Workbench is the JupyterLab IDE for ML on GCP - pre-installed ML frameworks, Vertex AI integration, and GPU support out of the box. Here's how to provision instances with Terraform including networking, IAM, auto-shutdown, and custom containers. In Series 1-3, we worked with managed AI services - Vertex AI for models, RAG Engine for retrieval, ADK for agents. Series 5 shifts to custom ML - training your own models, deploying endpoints, managing features, and building ML pipelines. It starts with a development environment. Vertex AI Workbench provides JupyterLab instances backed by Compute Engine VMs, pre-loaded with ML frameworks (TensorFlow, PyTorch, JAX, scikit-learn), the Vertex AI SDK, and direct integration with GCS, BigQuery, and Vertex AI services. Each instance is a full ML workspace. Terraform provisions them consistently across your team. 🎯 🏗️ Workbench Architecture Component What It Does Workbench Instance JupyterLab on a Compute Engine VM VM Image Pre-built with