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Last reviewed 2025-05-02 UTC
Architecture Center menyediakan referensi konten untuk berbagai subjek AI dan machine learning. Halaman ini memberikan informasi untuk membantu Anda mulai menggunakan AI generatif, AI tradisional, dan machine learning. Bagian ini juga
menyediakan daftar semua konten AI dan machine learning (ML) di
Architecture Center.
Mulai
Dokumen yang tercantum di halaman ini dapat membantu Anda mulai mendesain, membangun, dan men-deploy solusi AI dan ML di Google Cloud.
Mempelajari AI generatif
Mulailah dengan mempelajari dasar-dasar AI generatif di
Google Cloud, di situs dokumentasi Cloud:
Untuk mengidentifikasi kapan AI generatif, AI tradisional (yang mencakup prediksi dan klasifikasi), atau kombinasi keduanya mungkin sesuai dengan kasus penggunaan bisnis Anda, lihat Kapan harus menggunakan AI generatif atau AI tradisional.
Untuk mempelajari cetak biru AI generatif dan machine learning yang men-deploy pipeline untuk membuat model AI, lihat Membangun dan men-deploy model AI generatif dan machine learning di perusahaan. Panduan ini menjelaskan seluruh siklus proses pengembangan AI, mulai dari eksplorasi dan eksperimen data awal hingga pelatihan, deployment, dan pemantauan model.
Jelajahi contoh arsitektur berikut yang menggunakan AI generatif:
Google Cloud menyediakan
serangkaian layanan AI dan machine learning
untuk membantu Anda meringkas dokumen dengan AI generatif, membangun pipeline pemrosesan gambar, dan berinovasi dengan solusi AI generatif.
Terus menjelajah
Dokumen yang tercantum di bagian "AI dan machine learning" di navigasi kiri dapat membantu Anda membangun solusi AI atau ML. Dokumen disusun dalam kategori berikut:
AI Generatif: Merancang dan membangun solusi AI generatif.
Pelatihan model: Terapkan machine learning, federated learning, dan pengalaman cerdas yang dipersonalisasi.
MLOps: Menerapkan dan mengotomatiskan continuous integration, continuous delivery, dan continuous training untuk sistem machine learning.
Aplikasi AI dan ML: Bangun aplikasi di Google Cloud
yang disesuaikan untuk workload AI dan ML Anda.
[[["Mudah dipahami","easyToUnderstand","thumb-up"],["Memecahkan masalah saya","solvedMyProblem","thumb-up"],["Lainnya","otherUp","thumb-up"]],[["Sulit dipahami","hardToUnderstand","thumb-down"],["Informasi atau kode contoh salah","incorrectInformationOrSampleCode","thumb-down"],["Informasi/contoh yang saya butuhkan tidak ada","missingTheInformationSamplesINeed","thumb-down"],["Masalah terjemahan","translationIssue","thumb-down"],["Lainnya","otherDown","thumb-down"]],["Terakhir diperbarui pada 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."]]
