The AI Stack as seen by Google

This hierarchical view can be a great way to understand how the different components work together to deliver AI solutions within Google’s stack.

1. Infrastructure Layer

The foundational infrastructure layer is the bedrock of the entire AI stack, serving as the essential engine that provides the physical and virtual resources necessary to power sophisticated AI workloads.  It encompasses a suite of Google Cloud Platform (GCP) services meticulously designed for the unique demands of machine learning, including the high-performance computing resources like Cloud TPUs (Tensor Processing Units) and GPUs (Graphics Processing Units), which are specialized hardware accelerators critical for the computationally intensive tasks of training and inference. This layer also includes scalable storage solutions like Cloud Storage for managing the massive datasets that feed large language models, alongside robust networking capabilities that ensure efficient data transfer and communication between distributed compute nodes. By providing this powerful and flexible infrastructure, Google Cloud enables organizations to build, deploy, and scale their AI models without the need to manage complex, on-premise hardware.

• Key GCP Services:
  • Compute Engine: Provides virtual machines (VMs) that can be configured with powerful GPUs and Cloud TPUs (Tensor Processing Units), which are custom-built accelerators for machine learning workloads. These are essential for training and serving large-scale models.
  • Cloud Storage: Provides scalable and durable object storage to store the massive datasets required for training foundation models.
  • Google Kubernetes Engine (GKE): A managed Kubernetes service that simplifies the deployment, scaling, and management of containerized AI applications, making it easy to orchestrate complex workloads.
  • Networking: High-speed networking like Google Cloud’s Jupiter data center network ensures low-latency communication between compute resources, which is critical for distributed training.

2. Models Layer

The core of the AI stack is the Model Layer, consisting of the foundational models themselves. These models are not built for a single task; they are trained on vast, general-purpose datasets, enabling them to understand and generate a wide range of content, from text and images to code and data. This training process is computationally intensive and requires significant resources. These models serve as the fundamental building blocks, providing the core intelligence that developers can then fine-tune or integrate into new applications. Think of it as a raw, multi-purpose engine that can be adapted to power a car, a boat, or a generator. Without this foundational layer, the rest of the AI ecosystem could not function.

• Key Component:
  • Foundation Models: These are large-scale, pre-trained models, such as Gemini. They are distinguished by their versatility and ability to be adapted for a wide variety of tasks.
  • Model Garden: This is a curated hub within Vertex AI that provides access to a wide range of models, including Google’s own models (like Gemini), open-source models (like Llama), and third-party models. It acts as a starting point for developers to find, test, and tune models.
  • Model Tuning: At this layer, models can be customized using techniques like fine-tuning, which involves training a foundation model on a smaller, domain-specific dataset to improve its performance for a particular task.

3. Platform Layer

• Key GCP Services:
  • Vertex AI: This is Google Cloud’s unified Machine Learning Platform. It provides an end-to-end suite of tools for the entire ML lifecycle, from data preparation and model training to deployment and monitoring. It is the primary platform for using and customizing Gemini and other models.
  • Vertex AI Studio: A user-friendly, browser-based tool within Vertex AI for rapidly prototyping and experimenting with models, designing prompts, and testing new ideas.
  • Agent Builder: A suite of tools within Vertex AI for building, managing, and deploying AI agents. This platform allows you to create agents that can perform multi-step tasks by calling different tools and APIs.

4. AI Agents Layer

Building on the foundation of traditional large language models, AI agents represent a newer, more advanced layer where models are given the ability to take action. An AI agent is a software entity that is not simply reactive. It’s an intelligent entity that can reason, plan, and execute a series of tasks to achieve a specific goal. Unlike a simple chatbot that responds to a single query, an agent can break down a complex request, interact with external tools and systems (like a calendar, a web browser, or a database), and carry out a sequence of operations to complete the task. This makes it a proactive and goal-oriented system, shifting the AI paradigm from a passive information provider to an active problem solver.

• Key Concept:
  • Agents: Unlike a model that just generates a response, an agent can perform a sequence of actions.
    • For example ….. a travel agent could plan a trip by searching for flights, checking hotel availability, and booking reservations.
  • Agentic Capabilities: These are the abilities that allow an agent to use tools, access external data (like a company’s internal knowledge base), and perform complex workflows.
• Relationship to other layers: Agents are built on top of the Platform layer and utilize Foundation Models to power their reasoning and decision-making.

5. Generative AI Applications Layer

At the very top of the AI stack are the final consumer-facing or business-facing applications. These are the front-end solutions that users directly interact with. They represent the final product of the entire AI system. These applications are built on the foundational layers of the stack, leveraging the intelligence of models, the services of platforms, and the capabilities of underlying infrastructure. For example, a virtual assistant, a generative art tool, or a medical diagnostic application all fall into this category. The user doesn’t need to know about the complex layers below. They simply use the application to solve a problem or complete a task. This layer abstracts away all the technical complexity, delivering a seamless user experience.

• Key Characteristics:
  • User-facing: These applications provide a seamless user experience, powered by the AI stack below.
  • Use Cases: This is where the value of the entire stack is realized. Examples include:
    • Chatbots for customer service
    • Image generation tools for creative professionals
    • Code completion assistants for developers
    • Document summarization services