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本课程回顾了 Model Armor 的基本安全功能，并让您能够使用该服务。您将了解与 LLM 相关的安全风险，以及 Model Armor 如何保护您的 AI 应用。
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人工智能 (AI) 具备巨大的变革潜力，但也带来了新的安全挑战。本课程专为负责安全性和数据保护的领导者而设计，助其运用相关策略在组织内安全管理 AI。学习一个有助于实现以下目标的框架：主动识别并减轻 AI 特有的风险，保护敏感数据，确保遵从法规，构建弹性 AI 基础设施。通过四个不同行业的精选用例，探索这些策略如何应用于现实场景。
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本课程介绍 AI 隐私保护和安全方面的重要主题，还将探索使用 Google Cloud 产品和开源工具实施建议的 AI 隐私保护和安全实践的实用方法和工具。
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本课程介绍了 Responsible AI 的概念和 AI 原则，还介绍了在 AI/机器学习实践中识别公平性与偏见以及减少偏见的实用技巧，同时探索了使用 Google Cloud 产品和开源工具来实施 Responsible AI 最佳实践的实用方法和工具。
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本课程介绍了 AI 可解释性和透明度的相关概念，探讨了 AI 透明度对于开发者和工程师的重要性。同时探索了有助于在数据和 AI 模型中实现可解释性和透明度的实用方法及工具。
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On Google Cloud, building an app usually means handling more than just the code. You’ll need to deploy it, connect services, and keep things running properly. In this voyage, you’ll host a web app, set up a deployment pipeline, and build a REST API with Cloud Run. You’ll also try out Flutter and set up a Python development environment. You’ll work with VPC networks, load balancing, and clean up unused resources along the way. It’s a practical set of tasks that reflects how apps are actually run on Google Cloud.
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本课程能让机器学习从业者掌握评估生成式和预测式 AI 模型的基本工具、方法和最佳实践。要确保机器学习系统在实际运用中提供可靠、准确、高效的结果，做好模型评估至关重要。
学员将深入了解各项评估指标、方法及如何在不同模型类型和任务中适当应用这些指标和方法。课程将着重介绍生成式 AI 模型带来的独特挑战，并提供有效解决这些挑战的策略。通过利用 Google Cloud 的 Vertex AI Platform，学员可学习如何在模型选择、优化和持续监控工作中实施卓有成效的评估流程。
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本课程致力于为您提供所需的知识和工具，让您能够了解 MLOps 团队在部署和管理生成式 AI 模型以及探索 Vertex AI 如何帮助 AI 团队简化 MLOps 流程时面临的独特挑战，并帮助您在生成式 AI 项目中取得成功。
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78%—or nearly 8 in 10—business leaders say Google Cloud helps them stay ahead in the age of AI. A big part of that comes down to how teams build and connect intelligent systems. Here, you’ll build and manage conversational agents, and use speech-to-text and translation APIs to handle different types of input. You’ll also move from monolithic apps to microservices on GKE, connect workflows using webhooks, and use observability tools to keep track of what’s happening. IAM comes in as well to manage access where needed. It’s a practical look at how these pieces are used together in real setups.
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A lot of cloud work comes down to moving data, managing access, and making sense of what’s happening behind the scenes. In this trail, you’ll migrate a MySQL database to Google Cloud, work with IAM permissions using gcloud, and analyze network traffic with VPC Flow Logs. You’ll also store and manage media files, prepare data with Dataprep, and build reports using Looker Studio and LookML. There’s even a lab on measuring Speech-to-Text accuracy. It’s a mix of tasks that shows how data is handled, monitored, and used across different parts of Google Cloud.
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Google Kubernetes Engine (GKE) is all about running and managing containerized applications without worrying too much about the underlying setup. In this adventure, you’ll get hands-on with how things actually work—managing workloads, debugging issues, and trying out autoscaling. You’ll also explore Autopilot, run load tests, and work with private clusters and network access. By the end, you’ll have a clearer sense of how GKE setups are put together and handled in practice.
Model Armor：保障 AI 部署安全
Earned Apr 29, 2026 EDT
AI 时代安全性简介
Earned Apr 29, 2026 EDT
面向开发者的 Responsible AI：隐私保护和安全
Earned Apr 29, 2026 EDT
面向开发者的 Responsible AI：公平性与偏见
Earned Apr 29, 2026 EDT
面向开发者的 Responsible AI：可解释性和透明度
Earned Apr 29, 2026 EDT
Arcade Voyage: Modern Application Development
Earned Apr 29, 2026 EDT
利用 Vertex AI 实现机器学习运维 (MLOps)：模型评估
Earned Apr 28, 2026 EDT
适用于生成式 AI 的机器学习运维 (MLOps)
Earned Apr 28, 2026 EDT
Dialogue Design
Earned Apr 28, 2026 EDT
Arcade Trail: Data Migration
Earned Apr 28, 2026 EDT
Arcade Adventure: GKE Operations and Networking
Earned Apr 27, 2026 EDT