Rafael Costa
Member since 2023
Diamond League
8411 points
Add Agents to Gemini
Earned Oct 21, 2026 EDT
Govern Agent Access with Gemini platform
Earned Oct 20, 2026 EDT
Secure your Agents with Gemini Enterprise Agent Platform
Earned Oct 20, 2026 EDT
Govern agents with Agent Gateway, Agent Registry, and Policies
Earned Oct 6, 2026 EDT
Use a Third-Party Identity Provider with Workforce Identity Federation
Earned Oct 5, 2026 EDT
Model Armor：保護您部署的 AI 應用程式
Earned Oct 5, 2026 EDT
Deploy Gemini with Workspace Data Sources and Model Armor
Earned Oct 1, 2026 EDT
Improve Agent Search findings on Gemini platform
Earned Oct 1, 2026 EDT
Deploy Gemini to Transform Enterprises
Earned Oct 1, 2026 EDT
MLOps with Agent Platform: Manage Features
Earned Sep 24, 2026 EDT
在 Google Cloud 打造生成式 AI 應用程式
Earned Sep 11, 2026 EDT
開發人員的負責任 AI 技術：隱私權與安全性
Earned Sep 11, 2026 EDT
開發人員的負責任 AI 技術：可解釋性與透明度
Earned Sep 10, 2026 EDT
開發人員的負責任 AI 技術：公平性與偏誤
Earned Sep 9, 2026 EDT
使用 BigQuery ML 為預測模型進行資料工程
Earned Sep 3, 2026 EDT
運用 BigQuery ML 建立機器學習模型
Earned Sep 2, 2026 EDT
在 Google Cloud 為機器學習 API 準備資料
Earned Aug 31, 2026 EDT
Google Cloud 的 AI 和機器學習服務簡介
Earned Aug 24, 2026 EDT
Plan Change Management for Gemini Deployments
Earned Aug 10, 2026 EDT
機器學習運作 (MLOps) 與 Vertex AI：模型評估
Earned Jul 30, 2026 EDT
運用 Google Cloud 探索資料轉換功能
Earned Jul 27, 2026 EDT
生成式 AI 適用的機器學習運作 (MLOps)
Earned Jul 27, 2026 EDT
大型語言模型簡介
Earned Jul 27, 2026 EDT
透過 Google Cloud 進行數位轉型
Earned May 20, 2026 EDT
Build a Certification Study Guide: PMLE
Earned Mar 3, 2026 EST
生成式 AI 簡介
Earned Nov 6, 2023 EST
Cloud Hero Data Skills
Earned Nov 6, 2023 EST
In this challenge lab, you will demonstrate your ability to add agents to Gemini. 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 Gemini.
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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.
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As organizations rapidly deploy AI workforces, securing these autonomous systems becomes paramount. Secure Your Agents with Gemini Enterprise Agent Platform is a practical, security-first course designed to help you establish centralized, zero-trust control and comprehensive visibility over your AI ecosystem.
Instead of relying on theoretical frameworks, this course teaches you how to defend AI agents the way they are actually attacked—layer by layer, with identity and access at the absolute foundation.
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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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本課程將複習 Model Armor 的基本安全功能，讓您具備使用這項服務的能力。您將瞭解 LLM 的相關安全風險，以及 Model Armor 如何保護 AI 應用程式。
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In this challenge lab, you act as a Security Engineer deploying a secure Gemini 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
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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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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 introduces participants to MLOps tools and best practices for deploying, evaluating, monitoring and operating production ML systems on Google Cloud. MLOps is a discipline focused on the deployment, testing, monitoring, and automation of ML systems in production.
Learners will get hands-on practice using Agent Platform Feature Store's streaming ingestion at the SDK layer.
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大型語言模型 (LLM) 誕生之後，生成式 AI 應用程式帶來的嶄新使用者體驗，可說是幾乎前所未有。身為應用程式開發人員，您要如何在 Google Cloud，運用生成式 AI 建立出色的互動式應用程式？
本課程將帶您瞭解生成式 AI 應用程式，以及如何使用提示設計和檢索增強生成 (RAG)，透過 LLM 建構強大的應用程式。我們也會介紹可用於正式環境的生成式 AI 應用程式架構。您將建構採用 LLM 和 RAG 的對話應用程式。
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本課程涵蓋「AI 隱私權」和「AI 安全性」這兩個重要主題。我們將介紹實用的方法和工具，協助您運用 Google Cloud 產品和開放原始碼工具，導入 AI 隱私權和安全性的建議做法。
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本課程旨在說明 AI 的可解釋性和透明度概念、探討 AI 透明度對開發人員和工程師的重要性。課程中也會介紹實務方法和工具，有助於讓資料和 AI 模型透明且可解釋。
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本課程旨在說明負責任 AI 技術的概念和 AI 開發原則，同時介紹各項技術，在實務上找出公平性和偏誤，減少 AI/機器學習做法上的偏誤。我們也將探討實用方法和工具，透過 Google Cloud 產品和開放原始碼工具，導入負責任 AI 技術的最佳做法。
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完成使用 BigQuery ML 為預測模型進行資料工程技能徽章中階課程， 即可證明自己具備下列知識與技能：運用 Dataprep by Trifacta 建構連至 BigQuery 的資料轉換 pipeline； 使用 Cloud Storage、Dataflow 和 BigQuery 建構「擷取、轉換及載入」(ETL) 工作負載， 以及使用 BigQuery ML 建構機器學習模型。
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完成「運用 BigQuery ML 建立機器學習模型」技能徽章中階課程，即可證明您具備下列技能： 可使用 BigQuery ML 建立及評估機器學習模型，並根據資料進行預測。
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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。
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本課程介紹 Google Cloud 的 AI 和機器學習 (ML) 功能，著重說明如何開發生成式和預測式 AI 專案。我們也會探討「從資料到 AI」整個生命週期都適用的技術、產品和工具，並透過互動式練習，協助資料科學家、AI 開發人員和機器學習工程師精進專業知識。
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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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本課程針對評估生成式和預測式 AI 模型，向機器學習從業人員介紹相關的基礎工具、技術和最佳做法。模型評估是機器學習的重要領域，確保這類系統能在正式環境中提供可靠、準確且成效優異的結果。
學員將深入瞭解多種評估指標與方法，以及適用於不同模型類型和工作的應用方式。此外，也會特別介紹生成式 AI 模型帶來的獨特難題，並提供有效的應對策略。透過 Google Cloud Vertex AI 平台，學員將瞭解在模型挑選、最佳化和持續監控方面，該如何導入穩健的評估程序。
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雲端技術是強大的資產，與資料搭配後，將成為創新和提升客戶體驗的催化劑。「運用 Google Cloud 探索資料轉換功能」課程將探討機構如何運用雲端，讓資料更易於存取、做為行動依據和更有價值。
這門課程是 Cloud Digital Leader 學習路徑的一部分，旨在協助個人在職務上成長，並打造企業的未來。
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本課程旨在提供必要的知識和工具，協助您探索機器學習運作團隊在部署及管理生成式 AI 模型時面臨的獨特挑戰，並瞭解 Vertex AI 如何幫 AI 團隊簡化機器學習運作程序，打造成效非凡的生成式 AI 專案。
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這是一堂入門級的微學習課程，旨在探討大型語言模型 (LLM) 的定義和用途，並說明如何調整提示來提高 LLM 成效。此外，也會介紹多項 Google 工具，協助您自行開發生成式 AI 應用程式。
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數位轉型是現代組織必經的關鍵旅程，而建立穩固的雲端運算基礎，是跨出有意義創新的第一步。本課程「透過 Google Cloud 進行數位轉型」，將會介紹核心技術和策略框架，協助組織革新營運方式，並探討基本的雲端概念、全球網路基礎架構和共同責任模式，幫助主管在雲端之路上自信前行。本課程是 Cloud Digital Leader 學習路徑的一部分，旨在促進個人職能發展，並打造企業的未來。
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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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這個入門微學習課程主要說明生成式 AI 的定義和使用方式，以及此 AI 與傳統機器學習方法的差異。本課程也會介紹各項 Google 工具，協助您開發自己的生成式 AI 應用程式。
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Welcome Gamers! Learn Google Cloud Dataprep, create a streaming pipeline using a Google-Provided Cloud Dataflow template, work with gcloud Command Line, all while having fun! Extract entities from a snippit of text using the Cloud Natural Language API You will compete to see who can finish the game with the highest score. Earn the points by completing the steps in the lab.... and get bonus points for speed! Be sure to click "End" when you're done with each lab to get the maximum points. All players will be awarded the game badge.
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