Google Cloud Generative AI Stack Overview

The Google Cloud Generative AI Stack is a three-layer model that shows how all the pieces of a generative AI system connect. Think of it as a layered cake: each layer supports the one above it, and you can use the top layers without worrying about the bottom ones. This is a common pattern in cloud computing called "abstraction" — hiding complexity so that you can focus on the part you care about.

The bottom layer is the Infrastructure Layer. This is the physical hardware that runs everything. It includes thousands of specialised computers called TPUs (Tensor Processing Units), which are chips Google designed specifically for the massive maths operations required by AI models. It also includes GPUs (Graphics Processing Units), which were originally built for video games but are also very good at AI work because they can do many calculations at once. This layer also covers networking (the cables and routers that connect these chips together) and storage (the hard drives and solid-state drives that hold the training data and the finished model). An IT professional who works at this layer is called a Cloud Architect or Infrastructure Engineer. They are responsible for making sure there are enough chips to run a model, that the network is fast enough, and that the data is safe.

The middle layer is the Platform Layer. This is the layer of software tools that make it easier to build, train, and use AI models without having to manage any hardware. The main platform here is Vertex AI, which is Google Cloud's unified AI platform. Within Vertex AI, you get tools like: - Vertex AI Studio: A web-based interface where you can start building AI applications immediately, with no coding required. - Vertex AI Pipelines: A system to automate the steps of building a model (like data cleaning, training, and testing) so you can repeat them reliably. - Model Garden: A library of pre-built AI models that you can use or customise, including Google's own models like Gemma and third-party models like Llama. - Vertex AI Agent Builder: A tool to create conversational AI agents (chatbots) that can answer questions using your own company data. This layer also includes Cloud Storage for holding data and Cloud Logging for seeing what your AI application is doing. An IT professional working here might be called a Machine Learning Engineer or a Data Scientist. Their job is to use the tools in this layer to build a model that works well for their specific problem.

The top layer is the Application Layer. This is what end users actually see and interact with. It includes Google's own generative AI products like Gemini (the AI assistant you can chat with), Duet AI in Google Workspace (which helps you write emails or slides), and Vertex AI Search for Healthcare (a specialised search tool for medical records). It also includes any custom application that a company builds on top of the platform layer, such as a customer service bot for a bank or an image generator for a design agency. The people who work at this layer are Application Developers or Product Managers. Their focus is on user experience and making sure the AI does something useful.

The entire stack is built on the idea of managed services. That means Google Cloud takes care of the hardware and the underlying platform software, so you do not have to. For example, if you use Vertex AI to train a model, Google automatically handles finding available TPUs, provisioning them, and connecting them to your storage. If a chip fails, Google replaces it without you noticing. This is a huge change from the past, where companies had to buy their own servers, wire them together, and hire teams just to keep them running.

Each layer has its own security and access controls. For instance, Cloud IAM (Identity and Access Management) lets you decide who can see and use the models you build. Cloud KMS (Key Management Service) encrypts your data so that even Google Cloud administrators cannot read it. These security tools run across all three layers.

In summary, the stack lets different people focus on what they are best at. A hardware engineer can optimise the TPUs. A data scientist can use the platform tools to experiment with models. A business user can interact with the finished application. The exam will test that you understand these three layers, what each contains, and how they relate to each other.

Consider a medium-sized insurance company called "SafeGuard Insurance" that wants to build a generative AI tool for its claims adjusters. The tool should allow an adjuster to type something like "draft a denial letter for claim #4521 based on the policy exclusion for pre-existing roof damage" and have the AI produce a professional letter, pulling in the specific policy clauses and claim details.

An IT professional at SafeGuard, let us call her Priya (a Cloud Architect), needs to make this happen. Here is what she does step by step using the Google Cloud Generative AI Stack:

First, Priya does not buy any hardware. She never calls a vendor to order a server. Instead, she uses the Infrastructure Layer. She requests GPU capacity through the Google Cloud console. She selects a region (for example, us-central1 in Iowa) for data residency compliance. She sets up VPC (Virtual Private Cloud) networking so that the AI model can only communicate with SafeGuard's internal claims database. This entire step takes her about 30 minutes, whereas buying and installing physical hardware would have taken weeks.

Second, Priya moves to the Platform Layer. She opens Vertex AI Agent Builder and creates a new agent. She connects the agent to a data source: a Cloud Storage bucket containing thousands of past claim letters and the company's policy document. She does not train a model from scratch; instead, she uses Model Garden to select Gemini 1.5 Pro, a pre-existing Google model, and then she adds "grounding" by connecting it to SafeGuard's specific data. This process is called Retrieval Augmented Generation (RAG) — the AI retrieves the relevant policy and claim information from the database before generating its answer, ensuring the output is accurate and not made up.

Third, Priya uses Vertex AI Pipelines to set up a workflow. Whenever a new denial letter is drafted by the AI, the pipeline automatically sends it to a human adjuster for review (using Cloud Logging to track the audit trail). She sets up monitoring with Cloud Monitoring to watch for unusual activity, like the AI being asked to generate letters outside normal working hours.

Finally, Priya deploys the agent as a web application in the Application Layer. The adjusters access it through their existing web browser. They never see the platform or infrastructure. They just see a chat interface where they type their request and get a letter draft. Priya uses Cloud IAM to give access only to the 50 adjusters in the claims department.

The result: a custom generative AI tool built in a few days, using no hardware management, with built-in security and logging. The alternative, building everything from scratch, would have taken months and required a much larger team. This is the practical reality of the stack: it dramatically accelerates the time to get a generative AI application into production.