Nitin Kadam
成为会员时间：2021
黄金联赛
22596 积分
Build Enterprise Agents with Code Execution on Gemini platform
Earned Sep 19, 2026 EDT
Build Agents with the Agent Development Kit
Earned Sep 19, 2026 EDT
Cost Estimation and Optimization for Agentic Solutions
Earned Sep 18, 2026 EDT
Hill Climb Generative and Agentic Systems
Earned Sep 18, 2026 EDT
Upgrade to the Latest Gemini Models
Earned Sep 18, 2026 EDT
Evaluate Agents on Gemini platform
Earned Sep 18, 2026 EDT
Evaluate Generative and Agentic Systems
Earned Sep 18, 2026 EDT
Build with the Managed Agents API on Gemini platform
Earned Sep 18, 2026 EDT
Accelerate Agent Development with Antigravity and Agents CLI
Earned Sep 18, 2026 EDT
Govern agents with Agent Gateway, Agent Registry, and Policies
Earned Sep 18, 2026 EDT
Use a Third-Party Identity Provider with Workforce Identity Federation
Earned Sep 18, 2026 EDT
Deploy Gemini to Transform Enterprises
Earned Sep 17, 2026 EDT
Plan Change Management for Gemini Deployments
Earned Sep 17, 2026 EDT
Model Armor：保障 AI 部署安全
Earned Sep 16, 2026 EDT
Improve Agent Search findings on Gemini platform
Earned Sep 16, 2026 EDT
利用 Gemini 实现 AI 赋能的数据库管理
Earned Sep 8, 2026 EDT
Google Cloud Database Migrations: Strategy
Earned Sep 8, 2026 EDT
Build a Certification Study Guide: PMLE
Earned May 6, 2026 EDT
Agent Search Analytics on Gemini platform
Earned Dec 3, 2025 EST
Agent Search UI configurations on Agent Platform
Earned Dec 3, 2025 EST
生成式 AI 智能体：助力组织转型
Earned May 24, 2025 EDT
生成式 AI 应用：改变工作方式
Earned May 24, 2025 EDT
生成式 AI：全面了解生成式 AI
Earned May 24, 2025 EDT
生成式 AI：剖析基本概念
Earned May 24, 2025 EDT
生成式 AI：不只是聊天机器人
Earned May 24, 2025 EDT
Build a Certification Study Guide: PCA Exam Prep
Earned Mar 5, 2025 EST
Google Cloud 基础知识：核心基础设施
Earned Sep 13, 2024 EDT
Vertex AI Studio 简介
Earned Sep 10, 2024 EDT
Responsible AI: 和 Google Cloud 一起践行 AI 原则
Earned Sep 9, 2024 EDT
Generative AI for Business Leaders
Earned Sep 9, 2024 EDT
[DEPRECATED] Generative AI Fundamentals
Earned Sep 1, 2023 EDT
Transformer 模型和 BERT 模型
Earned Sep 1, 2023 EDT
编码器-解码器架构
Earned Sep 1, 2023 EDT
注意力机制
Earned Sep 1, 2023 EDT
图像生成简介
Earned Sep 1, 2023 EDT
负责任的 AI 简介
Earned Sep 1, 2023 EDT
大型语言模型简介
Earned Sep 1, 2023 EDT
生成式 AI 简介
Earned Sep 1, 2023 EDT
In this course, you explore the Code Execution feature of the Gemini platform, which lets AI agents safely generate and run Python code in isolated sandbox environments. You learn how the Agent Sandbox fits into the broader platform architecture, how to configure and operate Code Execution sandboxes using the Agent Platform SDK, and how to integrate code execution into agent workflows with the Agent Development Kit (ADK) in Gemini platform.
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In this course, you'll learn to use the Agent Development Kit (ADK) in Gemini platform to build systems where multiple AI agents collaborate on complex tasks. You'll start with the ADK agent model: how ADK represents agents, tools, and runners, and how a single agent is configured and run. You'll then make tools the model can call, persist session state across agents, and instrument the execution lifecycle with callbacks and plugins. Next, you'll orchestrate multiple agents using ADK's template workflow agents and graph-based workflows, and ground them in enterprise data through multi-source retrieval and MCP integrations. Finally, you'll deploy a multi-agent system to Agent Runtime as a managed service and register and share it through Gemini so users across an organization can reach it.
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In this course, you’ll learn how to estimate and manage GenAI solution costs, focusing on the unique unit economics of AI versus traditional cloud billing. You will learn to:
Understand Metering: Master the four Google Cloud GenAI metering categories and the token economy driving costs.
Estimate Costs: Build defensible estimates using the Pricing Calculator for models, context caching, and agentic components.
Optimize Spend: Leverage controls—such as input/output token reduction, caching, RAG efficiency, and model routing—while balancing cost, quality, and latency.
Implement Governance: Apply FinOps best practices, including budgets, quotas, cost attribution, and Provisioned Throughput management to control spend.
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In this course, you'll learn the disciplined process of hill climbing to transform your generative and agentic systems into reliable, high-performing tools. You'll examine the iterative 8-step loop by diagnosing system failures through trajectory analysis and applying precise interventions across the model, prompt, tool, and framework layers. By the end of this course, you'll be prepared to optimize system architecture and automate the hill climbing cycle while balancing quality, cost, and latency as first-class metrics.
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In this course, you'll learn how to establish a rigorous upgrade regression testing pipeline, safely deploy models using A/B testing and/or shadow mode, and address legacy prompt technical debt. You'll also learn the latest about Gemini 3 parameters including thinking levels and thought signatures, as well as new capabilities like media resolution controls and streaming function calls.
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In this course, you learn to evaluate, diagnose, and optimize AI agents on the Gemini platform. You begin where most teams begin: the agent runs, but you have no eval cases, no test data, and no production traffic to grade it with. From there you follow the Quality Flywheel, the evaluate-analyze-optimize loop at the center of Gemini platform. You instrument the agent so it emits the telemetry Gemini platform reads, generate eval cases by simulation, choose the metrics that grade them, run offline evaluations, monitor live traffic and alert on quality drift, then cluster failures and optimize. A final module covers build-time evaluation with the Agent Development Kit (ADK) in Gemini platform, which runs on your machine before you deploy.
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Throughout this course, you'll learn how to establish rigorous evaluation criteria, perform evaluations, design objective rubrics, calibrate autoraters, and simulate evaluation data. You'll gain the skills needed to design, execute, and scale a comprehensive evaluation plan that aligns system capabilities with organizational KPIs. This course is designed for technical practitioners, machine learning engineers, and software architects who build and deploy generative and agentic applications.
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In this course, you'll learn to build and run enterprise agents on the Managed Agents API, the managed agent runtime on Gemini platform. Three conceptual lessons give you the mental model. You'll learn why a real business task needs an agent rather than a chat model. You'll examine how the platform splits into a control plane that defines agents and a data plane that runs them. You'll learn how to assemble an agent from a definition, a sandboxed environment, mounted data, tools, and skills. Furthermore, you'll learn how to run it with background interactions, a streamed reason-act loop, resilient typed results, and state that persists across turns. You then put that model to work in a hands-on lab, where you build, run, and harden a retail merchandising agent for Cymbal Retail from an empty project to a production-shaped deployment. By the end, you'll be able to design, build, and operate a managed agent of your own.
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In this course, you’ll learn to simplify the creation of autonomous enterprise agents using the Agent Development Kit (ADK) in Gemini platform and you will learn how to leverage the power of Antigravity and the Agents CLI to transform your agent development workflow. In this course, you will explore practical techniques to automate repetitive tasks and significantly accelerate the creation of robust, enterprise-grade agents, ensuring scalability and efficiency in your AI projects.
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This course covers the technical side of governing an AI agent workforce using Agent Gateway, Agent Registry, and Policies. It discusses the overall architecture of centralized agent governance, focusing on managing agent sprawl and securing communication with internal systems. The course explores the infrastructure and enterprise networking required to deploy the Agent Gateway, including automated deployment with Terraform and configuring private egress via Private Service Connect. It offers guidance towards establishing a zero-trust security perimeter using unique agent identities with mTLS, per-tool authorization with REQUEST_AUTHZ, and granular CEL policies. Furthermore, it empowers administrators to implement content security guardrails using Model Armor in Gemini platform and Cloud DLP to mitigate semantic threats like prompt injection and data leakage. Finally, the course explains how to manage tool discovery through the Agent Registry and ensure end-to-end observability and thr…
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This course provides learners the knowledge to configure Workforce Identity Federation to grant Google Cloud and Gemini access to users that authenticate using a third-party Identity Provider. The curriculum includes theory and demo videos covering workforce identity pools, OIDC and SAML providers, attribute mapping, IAM policies, and troubleshooting guidance.
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This course covers the technical side of deploying Gemini. It discusses the overall architecture of Gemini, decisions to be made when provisioning the app infrastructure, and integrating enterprise data via Data Stores, Connectors. and Actions. The course also explores the kinds of agents that can be added to Gemini. It offers guidance towards establishing a security perimeter using IAM, VPC Service Control, Context-Aware Access, and semantic controls deployed with Model Armor in Gemini platform. Finally, it empowers administrators to fine-tune the end-user experience through comprehensive configuration options, and explains how administrators keep AI deployments secure, compliant, and performant through OpenTelemetry instrumentation and prompt logging.
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This course will guide you through designing customer engagements that result in successful, well-utilized Gemini deployments. Study the art of change management, how to identify and engage sponsors, recruit early adopters, help your customer identify and address cultural change challenges and skills gaps, and effectively deliver project communications. Additionally, learn how to support your customer to plan, offer, conduct, and evaluate end-user training, leverage partner resources, and create essential project documents.
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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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This course covers techniques for boosting and filtering search results, as well as the implementation of meta tags for advanced search control.
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本课程为数据库专业人员提供了一套可消除运营孤岛的战略框架。集成了 Gemini 的 Database Center 是一个针对您整个数据库资产的统一指挥控制台；借助该工具，您可以将全托管式服务与部署在 Compute Engine 上的自管数据库整合到一个可观测性信息中心内。本课程将助您掌握 Gemini 辅助的分析洞见功能，实现从被动式问题排查到主动式领导的转型，获得自动执行安全状况管理的能力，强制执行全组织范围的合规性标准，并在性能瓶颈对业务造成冲击之前完成性能优化。
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Ready to move your databases to Google Cloud? This course provides the strategy. Learn to evaluate business drivers to overcome high on-premise costs and vendor lock-in. Use Migration Center to discover assets, analyze TCO, and build a phased wave plan. You’ll learn to choose the right migration path and identify the right Google Cloud solutions for your workloads. You'll leave with a data-driven business case and a clear roadmap, ready to win executive buy-in and lead a successful migration.
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Learn how to use Gemini Notebook to create a personalized study guide for the Professional Machine Learning Engineer (PMLE) certification exam. You'll review Gemini Notebook features, create a notebook, and use the study guide to practice for a certification exam.
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AI Applications provides built-in analytics for your Agent Search and Gemini. Learn what metrics are tracked and how to view them in this course.
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Initial deployment of Agent Search and Gemini Enterprise apps takes only a few clicks, but getting the configurations right can elevate a deployment from a basic off-the-shelf app to an excellent custom search or recommendations experience.
In this course, you'll learn more about the many ways you can customize and improve search, recommendations, and Gemini Enterprise apps.
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“生成式 AI 智能体：助力组织转型”是“Gen AI Leader”学习路线中的第五门课程，也是最后一门课程。本课程探讨了组织如何使用量身定制的生成式 AI 智能体，帮助应对特定的业务挑战。您将亲自动手构建一个基本的生成式 AI 智能体，并探索这些智能体的组成部分，例如模型、推理循环以及各种工具。
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“生成式 AI 应用：改变工作方式”是 Generative AI Leader 学习路线的第四门课程。本课程介绍 Google 的生成式 AI 应用，例如 Gemini for Workspace 和 NotebookLM。它将引导您逐一了解接地、检索增强生成、构建有效提示和构建自动化工作流等概念。
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“生成式 AI：全面了解生成式 AI”是“Gen AI Leader”学习路线中的第三门课程。生成式 AI 正在改变我们的工作方式以及与世界的互动方式。作为领导者，应该如何利用生成式 AI 来推动实际业务成果？在本课程中，您将探索构建生成式 AI 解决方案的不同层级、Google Cloud 的产品/服务，以及选择解决方案时需要考虑的因素。
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生成式 AI：剖析基本概念是“Gen AI Leader”学习路线的第二门课程。在本课程中，您将探索 AI、机器学习和生成式 AI 之间的差异，了解各种数据类型如何赋能生成式 AI 来应对业务挑战，从而了解生成式 AI 的基本概念。您还将深入了解 Google Cloud 应对基础模型局限性的策略，以及负责任和安全的 AI 开发与部署面临哪些关键挑战。
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生成式 AI：不只是聊天机器人是 Gen AI Leader 学习路线的第一门课程，没有前提条件要求。本课程旨在带您超越对聊天机器人的浅层认知，深入挖掘生成式 AI 为您的组织带来的真正潜力。您将探索基础模型和提示工程等概念，它们对于利用生成式 AI 的强大功能至关重要。本课程还将提供指导，让您了解在为组织制定成功的生成式 AI 策略时，应考虑哪些重要因素。
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Learn how to use Gemini Notebook to create a personalized study guide for the Professional Cloud Architect certification exam.
You'll review Gemini Notebook features, create a notebook in Gemini Notebook, and learn how to use a study guide to practice for a certification exam.
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“Google Cloud 基础知识：核心基础设施”介绍在使用 Google Cloud 时会遇到的重要概念和术语。本课程通过视频和实操实验来介绍并比较 Google Cloud 的多种计算和存储服务，并提供重要的资源和政策管理工具。
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本课程介绍 Vertex AI Studio，这是一种用于与生成式 AI 模型交互、围绕业务创意进行原型设计并在生产环境中落地的工具。通过沉浸式应用场景、富有吸引力的课程和实操实验，您将探索从提示到产品的整个生命周期，了解如何将 Vertex AI Studio 用于多模态 Gemini 应用、提示设计、提示工程和模型调优。本课程的目的在于帮助您利用 Vertex AI Studio，在自己的项目中充分发掘生成式 AI 的潜力。
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随着企业对人工智能和机器学习的应用越来越广泛，以负责任的方式构建这些技术也变得更加重要。但对很多企业而言，真正践行 Responsible AI 并非易事。如果您有意了解如何在组织内践行 Responsible AI，本课程正适合您。
本课程将介绍 Google Cloud 目前如何践行 Responsible AI，以及从中总结的最佳实践和经验教训，便于您以此为框架构建自己的 Responsible AI 方法。
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A Business Leader in Generative AI can articulate the capabilities of core cloud Generative AI products and services and understand how they benefit organizations. This course provides an overview of the types of opportunities and challenges that companies often encounter in their digital transformation journey and how they can leverage Google Cloud's generative AI products to overcome these challenges.
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Earn a skill badge by passing the final quiz, you'll demonstrate your understanding of foundational concepts in generative AI.
A skill badge is a digital badge issued by Google Cloud in recognition of your knowledge of Google Cloud products and services. Share your skill badge by making your profile public and adding it to your social media profile.
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本课程向您介绍 Transformer 架构和 Bidirectional Encoder Representations from Transformers (BERT) 模型。您将了解 Transformer 架构的主要组成部分，例如自注意力机制，以及该架构如何用于构建 BERT 模型。您还将了解可以使用 BERT 的不同任务，例如文本分类、问答和自然语言推理。完成本课程估计需要大约 45 分钟。
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本课程简要介绍了编码器-解码器架构，这是一种功能强大且常见的机器学习架构，适用于机器翻译、文本摘要和问答等 sequence-to-sequence 任务。您将了解编码器-解码器架构的主要组成部分，以及如何训练和部署这些模型。在相应的实验演示中，您将在 TensorFlow 中从头编写简单的编码器-解码器架构实现代码，以用于诗歌生成。
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本课程将向您介绍注意力机制，这是一种强大的技术，可令神经网络专注于输入序列的特定部分。您将了解注意力的工作原理，以及如何使用它来提高各种机器学习任务的性能，包括机器翻译、文本摘要和问题解答。
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本课程向您介绍扩散模型。这类机器学习模型最近在图像生成领域展现出了巨大潜力。扩散模型的灵感来源于物理学，特别是热力学。过去几年内，扩散模型成为热门研究主题并在整个行业开始流行。Google Cloud 上许多先进的图像生成模型和工具都是以扩散模型为基础构建的。本课程向您介绍扩散模型背后的理论，以及如何在 Vertex AI 上训练和部署此类模型。
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这是一节入门级微课程，旨在解释什么是负责任的 AI、它的重要性，以及 Google 如何在自己的产品中实现负责任的 AI。此外，本课程还介绍了 Google 的 7 个 AI 开发原则。
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这是一节入门级微学习课程，探讨什么是大型语言模型 (LLM)、适合的应用场景以及如何使用提示调整来提升 LLM 性能，还介绍了可以帮助您开发自己的 Gen AI 应用的各种 Google 工具。
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这是一节入门级微课程，旨在解释什么是生成式 AI、它的用途以及与传统机器学习方法的区别。该课程还介绍了可以帮助您开发自己的生成式 AI 应用的各种 Google 工具。
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