AI Infrastructure: Storage Options
Earned Mar 24, 2026 EDT
Networking in Google Cloud: Fundamentals
Earned Mar 20, 2026 EDT
AI Infrastructure: Networking Techniques
Earned Mar 18, 2026 EDT
AI Infrastructure: Deployment Types
Earned Mar 18, 2026 EDT
AI 基礎架構：Cloud TPU
Earned Mar 15, 2026 EDT
AI 基礎架構：Cloud GPU
Earned Mar 14, 2026 EDT
AI 基礎架構：AI Hypercomputer 簡介
Earned Mar 11, 2026 EDT
Google Cloud 基礎知識：核心基礎架構
Earned Mar 5, 2026 EST
In this course, you’ll take a comprehensive journey through the storage solutions available on Google Cloud, specifically tailored for AI and high-performance computing (HPC) workloads. You’ll learn how to choose the right storage for each stage of the ML lifecycle. You’ll explore how to optimize for I/O performance during training, manage massive datasets for data preparation, and serve model artifacts with low latency. Through practical examples and demonstrations, you’ll gain the expertise to design robust storage solutions that accelerate your AI innovation.
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Networking in Google cloud is a 6 part course series. Welcome to the first course of our six part course series, Networking in Google Cloud: Fundamentals.
This course provides a comprehensive overview of core networking concepts, including networking fundamentals, virtual private clouds (VPCs), and the sharing of VPC networks. Additionally, the course covers network logging and monitoring techniques.
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Welcome to the "AI Infrastructure: Networking Techniques" course. In this course, you'll learn to leverage Google Cloud's high-bandwidth, low-latency infrastructure to optimize data transfer and communication between all the components of your AI system. By the end, you'll grasp the critical role networking plays across the entire AI pipeline from data ingestion and training to inference and be able to apply best practices to ensure your workloads run at maximum speed.
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This course provides a comprehensive guide to deploying, managing, and optimizing AI and high-performance computing (HPC) workloads on Google Cloud. Through a series of lessons and practical demonstrations, you’ll explore diverse deployment strategies, ranging from highly customizable environments using Google Compute Engine (GCE) to managed solutions like Google Kubernetes Engine (GKE). Specifically, you’ll learn how to create clusters and deploy GKE for inference.
想瞭解 AI 背後的強大硬體嗎？本單元將深入解析針對效能最佳化的 AI 電腦，說明其重要性。我們將探討 CPU、GPU 和 TPU 如何大幅加速 AI 任務運算，分析各自的特點，以及 AI 軟體如何充分利用這些硬體效能。單元結束後，您將清楚掌握如何根據 AI 專案挑選合適的 GPU，並做出明智的 AI 工作負載決策。
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準備開始使用 AI Hypercomputer 了嗎？這門課程可讓您快速上手！我們將介紹這個架構的基本概念，以及此架構如何幫助 AI 處理 AI 工作負載。您將瞭解 Hypercomputer 內的不同元件，例如 GPU、TPU 和 CPU，以及如何視需求選擇合適的部署方法。
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「Google Cloud 基礎知識：核心基礎架構」介紹了在使用 Google Cloud 時會遇到的重要概念和術語。本課程會透過影片和實作實驗室，介紹並比較 Google Cloud 的多種運算和儲存服務，同時提供重要的資源和政策管理工具。
AI Infrastructure: Storage Options
Earned Mar 24, 2026 EDT
Networking in Google Cloud: Fundamentals
Earned Mar 20, 2026 EDT
AI Infrastructure: Networking Techniques
Earned Mar 18, 2026 EDT
AI Infrastructure: Deployment Types
Earned Mar 18, 2026 EDT
AI 基礎架構：Cloud TPU
Earned Mar 15, 2026 EDT
AI 基礎架構：Cloud GPU
Earned Mar 14, 2026 EDT
AI 基礎架構：AI Hypercomputer 簡介
Earned Mar 11, 2026 EDT
Google Cloud 基礎知識：核心基礎架構
Earned Mar 5, 2026 EST