The Architecture Center provides content resources across a wide variety of AI and machine learning subjects. This page provides information to help you get started with generative AI, traditional AI, and machine learning. It also provides a list of all the AI and machine learning (ML) content in the Architecture Center.
Get started
The documents listed on this page can help you get started with designing, building, and deploying AI and ML solutions on Google Cloud.
Explore generative AI
Start by learning about the fundamentals of generative AI on Google Cloud, on the Cloud documentation site:
- To learn the stages of developing a generative AI application and explore the products and tools for your use case, see Build a generative AI application on Google Cloud.
- To identify when generative AI, traditional AI (which includes prediction and classification), or a combination of both might suit your business use case, see When to use generative AI or traditional AI.
- To define an AI business use case with a business value-driven decision approach, see Evaluate and define your generative AI business use case.
- To address the challenges of model selection, evaluation, tuning, and development, see Develop a generative AI application.
To explore a generative AI and machine learning blueprint that deploys a pipeline for creating AI models, see Build and deploy generative AI and machine learning models in an enterprise. The guide explains the entire AI development lifecycle, from preliminary data exploration and experimentation through model training, deployment, and monitoring.
Browse the following example architectures that use generative AI:
- Generative AI document summarization
- Generative AI knowledge base
- Generative AI RAG with Cloud SQL
- Infrastructure for a RAG-capable generative AI application using Vertex AI and Vector Search
- Infrastructure for a RAG-capable generative AI application using Vertex AI and AlloyDB for PostgreSQL
- Infrastructure for a RAG-capable generative AI application using GKE
- Model development and data labeling with Google Cloud and Labelbox
For information about Google Cloud generative AI offerings, see Vertex AI and running your foundation model on GKE.
Design and build
To select the best combination of storage options for your AI workload, see Design storage for AI and ML workloads in Google Cloud.
Google Cloud provides a suite of AI and machine learning services to help you summarize documents with generative AI, build image processing pipelines, and innovate with generative AI solutions.
Keep exploring
The documents that are listed in the "AI and machine learning" section of the left navigation can help you build an AI or ML solution. The documents are organized in the following categories:
- Generative AI: Design and build generative AI solutions.
- Model training: Implement machine learning, federated learning, and personalized intelligent experiences.
- MLOps: Implement and automate continuous integration, continuous delivery, and continuous training for machine learning systems.
- AI and ML applications: Build applications on Google Cloud that are customized for your AI and ML workloads.