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Last reviewed 2025-05-02 UTC
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 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 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:
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.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Hard to understand","hardToUnderstand","thumb-down"],["Incorrect information or sample code","incorrectInformationOrSampleCode","thumb-down"],["Missing the information/samples I need","missingTheInformationSamplesINeed","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2025-05-02 UTC."],[[["\u003cp\u003eThis Architecture Center page offers resources for understanding and implementing AI and machine learning (ML) solutions, including generative AI, traditional AI, and MLOps.\u003c/p\u003e\n"],["\u003cp\u003eThe content provides guidance on designing, building, and deploying AI and ML solutions on Google Cloud, with a focus on various generative AI applications and use cases.\u003c/p\u003e\n"],["\u003cp\u003eThe page includes a range of example architectures, like document summarization and knowledge bases, as well as RAG implementations across several products like Cloud SQL, Vertex AI, and GKE.\u003c/p\u003e\n"],["\u003cp\u003eResources are categorized into generative AI, model training, MLOps, and AI/ML applications, offering a comprehensive overview of available content.\u003c/p\u003e\n"],["\u003cp\u003eYou can explore various AI/ML solutions and filter resources based on product names or descriptions to find relevant information for specific needs and build your AI and ML applications.\u003c/p\u003e\n"]]],[],null,["# AI and machine learning resources\n\n\u003cbr /\u003e\n\nThe Architecture Center provides content resources across a wide variety of AI\nand machine learning subjects. This page provides information to help you get\nstarted with generative AI, traditional AI, and machine learning. It also\nprovides a list of all the AI and machine learning (ML) content in the\nArchitecture Center.\n\nGet started\n-----------\n\nThe documents listed on this page can help you get started with designing,\nbuilding, and deploying AI and ML solutions on Google Cloud.\n\n### Explore generative AI\n\nStart by learning about the fundamentals of generative AI on\nGoogle Cloud, on the Cloud documentation site:\n\n- 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](/docs/ai-ml/generative-ai).\n- 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](/docs/ai-ml/generative-ai/generative-ai-or-traditional-ai).\n- To define an AI business use case with a business value-driven decision approach, see [Evaluate and define your generative AI business use case](/docs/ai-ml/generative-ai/evaluate-define-generative-ai-use-case).\n- To address the challenges of model selection, evaluation, tuning, and development, see [Develop a generative AI application](/docs/ai-ml/generative-ai/develop-generative-ai-application).\n\nTo 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](/architecture/blueprints/genai-mlops-blueprint). The guide explains the entire AI development lifecycle, from preliminary data exploration and experimentation through model training, deployment, and monitoring.\n\nBrowse the following example architectures that use generative AI:\n\n- [Generative AI document summarization](/architecture/ai-ml/generative-ai-document-summarization)\n- [Generative AI knowledge base](/architecture/ai-ml/generative-ai-knowledge-base)\n- [Generative AI RAG with Cloud SQL](/architecture/ai-ml/generative-ai-rag)\n- [Infrastructure for a RAG-capable generative AI application using Vertex AI and Vector Search](/architecture/gen-ai-rag-vertex-ai-vector-search)\n- [Infrastructure for a RAG-capable generative AI application using Vertex AI and AlloyDB for PostgreSQL](/architecture/rag-capable-gen-ai-app-using-vertex-ai)\n- [Infrastructure for a RAG-capable generative AI application using GKE and Cloud SQL](/architecture/rag-capable-gen-ai-app-using-gke)\n- [Model development and data labeling with Google Cloud and Labelbox](/architecture/partners/model-development-data-labeling-labelbox-google-cloud)\n\nFor information about Google Cloud generative AI offerings, see\n[Vertex AI](/vertex-ai/generative-ai/docs/multimodal/overview)\nand\n[running your foundation model on GKE](/kubernetes-engine/docs/integrations/ai-infra).\n\n### Design and build\n\nTo select the best combination of storage options for your AI workload, see\n[Design storage for AI and ML workloads in Google Cloud](/architecture/ai-ml/storage-for-ai-ml).\n\nGoogle Cloud provides a\n[suite of AI and machine learning services](/products/ai)\nto help you summarize documents with generative AI, build image processing\npipelines, and innovate with generative AI solutions.\n\nKeep exploring\n--------------\n\nThe documents that are listed in the \"AI and machine learning\" section of the\nleft navigation can help you build an AI or ML solution."]]
