Vivien Woo
Member since 2023
Diamond League
10010 points
Cost Estimation and Optimization for Agentic Solutions
Earned Jul 20, 2026 EDT
Hill Climb Generative and Agentic Systems
Earned Jul 20, 2026 EDT
Upgrade to the Latest Gemini Models
Earned Jul 20, 2026 EDT
Evaluate Agents on Gemini Enterprise Agent Platform
Earned Jul 20, 2026 EDT
Evaluate Generative and Agentic Systems
Earned Jul 20, 2026 EDT
Build Enterprise Agents with Code Execution on Gemini Enterprise Agent Platform
Earned Jul 16, 2026 EDT
Build Agents with the Agent Development Kit
Earned Jul 16, 2026 EDT
Build with the Managed Agents API on Gemini Enterprise Agent Platform
Earned Jul 16, 2026 EDT
Govern Agent Access with Gemini Enterprise Agent Platform
Earned Jul 16, 2026 EDT
Add Agents to Gemini Enterprise
Earned Jul 16, 2026 EDT
Deploy Gemini Enterprise with Workspace Data Sources and Model Armor
Earned Jul 15, 2026 EDT
Evaluate and Improve Agent Development Kit Agents
Earned Jul 14, 2026 EDT
Deploy an Agent with Agent Development Kit (ADK)
Earned Jul 6, 2026 EDT
Accelerate Development with Antigravity
Earned Jul 3, 2026 EDT
Accelerate Agent Development with Antigravity and Agents CLI
Earned Jul 3, 2026 EDT
Build a Certification Study Guide: PCA Exam Prep
Earned Jun 13, 2026 EDT
生成式 AI 代理：實現組織轉型
Earned Jun 11, 2026 EDT
生成式 AI 應用程式：徹底改變工作方式
Earned Jun 11, 2026 EDT
生成式 AI：掌握幕後技術與環境
Earned Jun 11, 2026 EDT
生成式 AI：瞭解基礎概念
Earned Jun 11, 2026 EDT
生成式 AI：不只是聊天機器人
Earned Jun 11, 2026 EDT
在 Google Cloud 為機器學習 API 準備資料
Earned Jan 29, 2023 EST
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.
Learn more
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.
Learn more
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.
Learn more
In this course, you learn to evaluate, diagnose, and optimize AI agents on the Gemini Enterprise Agent Platform (GEAP). 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 GEAP. You instrument the agent so it emits the telemetry GEAP 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), which runs on your machine before you deploy.
Learn more
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.
Learn more
In this course, you explore the Code Execution feature of the Gemini Enterprise Agent 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).
Learn more
In this course, you'll learn to use the Agent Development Kit (ADK) 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 Enterprise so users across an organization can reach it.
Learn more
In this course, you'll learn to build and run enterprise agents on the Managed Agents API, the managed agent runtime on Gemini Enterprise Agent 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. And 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.
Learn more
In this challenge lab, you will act as a cloud engineer supporting the Cymbal Pools finance team. Your mission is to deploy a BigQuery-enabled agent to Agent Runtime to help process invoice data using natural language.
Rather than building from scratch, you inherit an unsecured deployment. You must establish basic data governance by configuring the Agent Development Kit (ADK), deploying the agent with a dedicated SPIFFE identity, identifying permission blocks, and applying least-privilege IAM roles so the agent can safely query and update the BigQuery database from the Agent Runtime Playground.
Learn more
In this challenge lab, you will demonstrate your ability to add agents to a Gemini Enterprise app. You will build an agent with Agent Designer. And you will build a no-code agent with Agent Development Kit, deploy it to Agent Engine, and add it to the Gemini Enterprise app.
Learn more
In this challenge lab, you act as a Security Engineer deploying a secure Gemini Enterprise environment for Cymbal Bank. You will ground Gemini in web search and internal Workspace sources to ensure accurate, contextual responses. To maintain compliance, you will configure Model Armor policies to filter sensitive data and block threats like prompt injections and malicious URLs. Finally, you will manage specific end-user features to customize the AI experience safely
Learn more
Complete the Evaluate and Improve Agent Development Kit Agents skill badge to demonstrate your ability to use ADK's evaluation tools to "hill climb" — making measurable, iterative improvements to an agent. You will run an initial evaluation to establish a baseline, apply optimization techniques, and re-evaluate the agent to measure your success.
Learn more
In this challenge lab, you will demonstrate your ability to author agents using Agent Development Kit (ADK), deploy those agents to Agent Engine, and use them from a web app. Complete the challenge lab to earn a Google Cloud skill badge.
Learn more
Complete the Accelerate Development with Antigravity skill badge to demonstrate your proficiency in using the Antigravity IDE for developing agentic workflows. You will be tasked with configuring an MCP server, authoring custom agent skills and rules, prototyping with the Agents CLI, and deploying to the Google Cloud Agent Runtime.
A skill badge is an exclusive digital badge issued by Google Cloud in recognition of your proficiency with Google Cloud products and services and tests your ability to apply your knowledge in an interactive hands-on environment. Complete the assessment challenge lab, to receive a skill badge that you can share with your network.
When you complete this course, you can earn the badge displayed here and claim it on Credly! Boost your cloud career by showing the world the skills you have developed!
Learn more
In this course, you’ll learn to simplify the creation of autonomous enterprise agents using the Agent Development Kit (ADK) 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.
Learn more
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.
Learn more
「生成式 AI 代理：實現組織轉型」是 Gen AI Leader 學習路徑的第五堂也是最後一堂課程。本課程將探討組織如何運用自訂生成式 AI 代理，解決特定的業務難題。您將動手練習建構基本的生成式 AI 代理，同時探索這類代理的各種元件，例如模型、推論迴圈和工具。
Learn more
「生成式 AI 應用程式：徹底改變工作方式」是 Generative AI Leader 學習路徑的第四門課程。本課程將介紹 Google 的生成式 AI 應用程式,例如 Gemini for Workspace 和NotebookLM,也會引導您瞭解各種概念,像是建立基準、檢索增強生成、建構有效的提示詞,以及打造自動化工作流程等。
Learn more
「生成式 AI：掌握幕後技術與環境」是 Gen AI Leader 學習路徑的第三門課程。生成式 AI 正在改變我們的工作方式，以及與周遭世界的互動模式。身為領導者，您要如何駕馭其強大的功能，創造實際的業務成果？在本課程中，您將認識建構生成式 AI 解決方案時的各個層面、Google Cloud 產品與服務，以及選擇解決方案時應考量的因素。
Learn more
生成式 AI：瞭解基礎概念是 Gen AI Leader 學習路徑的第二門課程。在本課程中，您將瞭解 AI、機器學習和生成式 AI 的差異，以及各種類型的資料如何協助生成式 AI 解決業務難題，進而掌握生成式 AI 的基礎概念。您還能深入瞭解 Google Cloud 因應基礎模型限制的策略，以及開發、部署安全且負責任的 AI 技術時面臨的主要挑戰。
Learn more
「生成式 AI：不只是聊天機器人」是 Gen AI Leader 學習路徑的第一門課程，沒有先修條件。本課程旨在帶領您超越對聊天機器人的基本認識，探索生成式 AI 能為貴組織帶來的真正潛力。您將瞭解基礎模型和提示工程等概念，這些都是發揮生成式 AI 威力的關鍵。本課程也會引導您思考重要事項，協助您為貴組織制定成功的生成式 AI 策略。
Learn more
完成 在 Google Cloud 為機器學習 API 準備資料 技能徽章入門課程，即可證明您具備下列技能： 使用 Dataprep by Trifacta 清理資料、在 Dataflow 執行資料管道、在 Managed Service for Apache Spark 建立叢集和執行 Apache Spark 工作，以及呼叫機器學習 API，包含 Cloud Natural Language API、Google Cloud Speech-to-Text API 和 Video Intelligence API。
Learn more
