Description
You search the Vertex AI docs for how to serve a model, expecting three links, and forty appear. There is an agent platform, a full MLOps stack, its own serving infrastructure, a shelf of specialized services, and four of them look like they would do the job. You pick one, wire it up for a week, and find out you were paying for training machinery to run a single inference you could have made with one API call. The hard part of Vertex was never any one service. It is that nobody handed you the map: which one solves the problem in front of you, how they fit together, and where you are about to buy machinery you do not need.
This guide is a working map of Vertex AI, the part of Google Cloud where you build, train, serve, and run AI, from the foundation models in Model Garden to the agent platform, the MLOps stack, the serving layer, and the specialized services for search, vision, speech, and documents. Vertex has grown into dozens of products, and the real difficulty is not learning any one of them but knowing which to reach for, how they connect, and which you can safely ignore for the job in front of you. The book does not narrate every button. It walks the platform in the order you actually adopt it: where to test a model before you commit, the three ways to build an agent and when each fits, the plumbing that makes agents stateful and governed, the lab and lineage tools that keep training reproducible, how to train only when a foundation model will not do, how to serve predictions at scale and search vectors fast, how to watch a model in production, and what the specialized services buy you over building it yourself. Each chapter leaves you able to choose, not just name, the service that fits. For engineers and architects who can already write the code and now need to navigate the platform without overbuying or getting lost.
Who this book is for
This guide is for: engineers and architects on Google Cloud who need to build, train, serve, and operate AI on Vertex AI and want to know which of its many services to reach for, and which to skip, for each job.
The shortcut nobody hands you
The 5 Steps to Becoming the One Who Calls the Vertex AI Stack Without Overspending. The working map that turns dozens of Vertex AI services into a platform you can navigate. Instead of learning every product, you learn the terrain, so for any job in front of you, you know which region owns it, which service to reach for, and which machinery to walk right past. Go from lost in forty links to the engineer who chooses the fit in minutes, without overbuying or getting lost.
Every chapter, laid out
- Chapter 1: The day forty links appeared where you expected three
- Chapter 2: Vertex AI is four countries, and you live in only one
- Chapter 3: Why teams pay a year for the wrong model
- Chapter 4: The build decision that quietly costs you weeks
- Chapter 5: The agent that greeted a returning user like a stranger
- Chapter 6: The ticket that tries to talk your agent into wiring money
- Chapter 7: final, final2, final_real: the model nobody could reproduce
- Chapter 8: Do you need to train a model, or just use one?
- Chapter 9: The morning someone skipped a step and shipped a worse model
- Chapter 10: The nightly job that ran up an always-on bill
- Chapter 11: When an accurate model turns into a confident liar
- Chapter 12: A quarter of training, or one API call
- Chapter 13: Why the engineer who ships says no ninety times
