I just went through Chapter 2 – Google Cloud Basics from the book Ultimate Generative AI Solutions on Google Cloud, and it’s packed with practical knowledge that most online tutorials skip.

Here are 7 key things I learned that you can apply right now if you're planning to build or scale with GenAI on GCP:

✅ 1. Set up your GCP the right way

→ Many skip this. The chapter guides you through clean setup, billing control, and project structuring — so you don’t hit a wall later with quota issues or mismanaged resources.

✅ 2. Understand the GCP Console & SDK

→ Learn how to move between the web interface and command-line like a pro. If you’re serious about automation and deployments, this is foundational.

✅ 3. Start Exploring Gemini (Google’s GenAI)

→ Access it via both UI and API — the chapter shows you how. Perfect if you're building apps or chatbots that integrate LLM capabilities.

✅ 4. Use Vertex AI Studio Efficiently

→ Most people open Vertex AI and get overwhelmed. This breaks down the process: from logging in to creating your first GenAI model experiment.

Book link: https://amzn.in/d/0ldX0X1

✅ 5. Learn Serverless with Cloud Functions

→ No need to manage infrastructure. Trigger AI processes via functions—scalable, cost-effective, and beginner-friendly.

✅ 6. Move into Containerization with Cloud Run

→ If you're deploying AI models or microservices, Cloud Run is a hidden gem.

✅ 7. Don’t Ignore Monitoring (Seriously)

→ Cloud Monitoring & Trace help you debug and optimize usage. Especially helpful when you scale AI usage and want to keep costs + performance in check.

🧠 Why this chapter matters:

It's not just theory. You get a roadmap to actually build. If you're a startup founder, developer, or digital business owner trying to harness GenAI — this will save you hours of trial and error.

📘 Grab the book if you're serious about using GenAI + Google Cloud effectively. It’s not just a read — it’s a reference manual.

Book link: https://amzn.in/d/0ldX0X1

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