Google Cloud (GCP)
The platform basics every GCP engineer must know cold — projects, regions and zones, billing — plus the services for deploying ML/AI workloads and the generative-AI and agent stack.
Questions on Google Cloud from the ground up: how resources are organised and billed, where they physically run, which compute services you deploy models and agents on, and how the managed generative-AI and agent services (Vertex AI / Gemini Enterprise Agent Platform, Agent Development Kit, MCP, A2A) fit together. Written for someone who already builds on GCP and wants the basics to be second nature.
Subtopics
- Foundations: projects, regions, zones, and billingMid / SeniorThe resource hierarchy, what a project really is, regions vs zones vs multi-regions, how billing accounts, budgets, and pricing models work.5 concepts
- Deploying ML and AI workloadsMid / SeniorChoosing between Cloud Run, GKE, Vertex AI endpoints, Compute Engine, Batch and other services for serving models, running training, and hosting AI apps — with GPUs, scaling, and cost trade-offs.7 concepts
- Generative AI and agent servicesMid / SeniorGemini on Vertex AI, Model Garden, grounding, RAG and vector search, the Agent Development Kit, managed agent runtime, MCP and A2A, and Gemini Enterprise — how the GCP generative-AI and agentic stack fits together.8 concepts