Petr Klimeš
Member since 2026
Silver League
11202 points
Security Command Center: Vulnerabilities and Misconfigurations
Earned Nov 3, 2026 EST
Google Threat Intelligence
Earned Nov 3, 2026 EST
生成式 AI 適用的機器學習運作 (MLOps)
Earned Nov 3, 2026 EST
Introduction Google Security Operations (SOAR)
Earned Nov 3, 2026 EST
Introduction to Google Security Operations (SIEM)
Earned Nov 3, 2026 EST
運用工具讓 AI 代理具備所要能力
Earned Nov 3, 2026 EST
Machine Learning Operations (MLOps): Getting Started
Earned Nov 3, 2026 EST
機器學習運作 (MLOps) 與 Vertex AI：模型評估
Earned Nov 3, 2026 EST
Google Security Operations - Deep Dive
Earned Nov 3, 2026 EST
Google DeepMind : 08 Capstone: Develop Your Model for Real-World Impact
Earned Nov 3, 2026 EST
Orchestrate Workflows with the Data Agent Kit
Earned Nov 3, 2026 EST
Customer Experience Agent Studio: Design and Test Solutions
Earned Nov 3, 2026 EST
Google Cloud Agent Governance and Security
Earned Nov 3, 2026 EST
Innovating with Data and Google Cloud
Earned Nov 3, 2026 EST
Google DeepMind: 01 Build Your Own Small Language Model
Earned Nov 3, 2026 EST
Google DeepMind: 02 Represent Your Language Data
Earned Nov 3, 2026 EST
Google DeepMind: 03 Design And Train Neural Networks
Earned Nov 3, 2026 EST
Google DeepMind: 04 Discover The Transformer Architecture
Earned Nov 3, 2026 EST
Google DeepMind: 07 Accelerate Your Model
Earned Nov 3, 2026 EST
Google DeepMind: 05 Fine-Tune Your Model
Earned Nov 3, 2026 EST
管理 AI 代理的記憶與狀態
Earned Nov 3, 2026 EST
Add Agent Guardrails with Callbacks
Earned Nov 3, 2026 EST
Build a Certification Study Guide: PCA Exam Prep
Earned Nov 3, 2026 EST
部署您的第一個代理
Earned Nov 3, 2026 EST
Coordinate Multiple Agents
Earned Nov 3, 2026 EST
Agent Assist 及其生成式 AI 功能
Earned Nov 3, 2026 EST
依據生成式應對手冊建立代理
Earned Nov 3, 2026 EST
AI Boost Bites：打造自己的懷舊街機遊戲
Earned Oct 16, 2026 EDT
AI Boost Bites：變身「氛圍 DJ」
Earned Oct 16, 2026 EDT
AI Boost Bites：製作最棒的大學選課報告
Earned Oct 16, 2026 EDT
AI Boost Bites：一鍵生成廣告活動視覺素材
Earned Oct 16, 2026 EDT
AI Boost Bites：從草稿到功能完整的應用程式
Earned Oct 16, 2026 EDT
AI Boost Bites: Notes to Sheets with Gemini
Earned Oct 16, 2026 EDT
AI Boost Bites：在 Gmail 中使用 Gemini 安排日曆的訣竅
Earned Oct 16, 2026 EDT
AI Boost Bites: Build Slides Fast with Gemini
Earned Oct 16, 2026 EDT
AI Boost Bites: Talk to Your Data in Sheets
Earned Oct 16, 2026 EDT
AI Boost Bites: AI Magic in a Sheets Cell
Earned Oct 16, 2026 EDT
AI Boost Bites：無程式碼試算表和指令碼
Earned Oct 16, 2026 EDT
AI Boost Bites：運用 Gemini 和 Apps Script 自動執行工作
Earned Oct 16, 2026 EDT
AI Boost Bites: Advanced Analysis in Sheets
Earned Oct 16, 2026 EDT
建立您的第一個 Gemini Enterprise 應用程式
Earned Oct 16, 2026 EDT
企業 AI 代理與用途
Earned Oct 16, 2026 EDT
AI 代理基礎知識
Earned Oct 16, 2026 EDT
生成式 AI 簡介
Earned Oct 15, 2026 EDT
Learn how to use Security Command Center (SCC) to triage vulnerabilities and misconfigurations identified by platform security detectors.
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Google Threat Intelligence 在掌握威脅方面首屈一指，為全球的資安團隊提供詳細、及時的威脅情報。這個課程介紹 Google Threat Intelligence 的多項功能，以及組織可以透過哪些常見方法，運用這項產品主動減輕威脅。
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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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This course introduces the SOAR component of Google SecOps, guiding learners through setup, response automation, and reporting. With demos, hands-on examples, and quizzes, participants will learn how to simplify investigations and make day-to-day security operations more efficient. The course is designed around short videos, averaging about five minutes each, divided into granular topics to support different learning styles and fit into the time learners have available.
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This course provides a comprehensive, end-to-end exploration of Google SecOps SIEM, guiding learners from foundational concepts to advanced investigation and detection engineering. Participants will gain hands-on experience with data ingestion, normalization, RBAC configuration, searching, and dashboarding using both legacy and native capabilities. Through structured modules, demos, and curated examples, the course emphasizes real-world investigation workflows, UDM-based analytics, and YARA-L rule development. By the end of the course, learners will be equipped to operationalize SIEM effectively within their environment and build scalable processes for detection, investigation, and reporting.
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您已建立具有進階設定功能的代理，可開始賦予代理實際應用能力。為代理配備工具，使代理能搜尋網路、執行程式碼、查詢資料庫和執行自訂動作。將代理從聰明的回覆者，轉變為能採取行動的得力助手。
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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. Machine Learning Engineering professionals use tools for continuous improvement and evaluation of deployed models. They work with (or can be) Data Scientists, who develop models, to enable velocity and rigor in deploying the best performing models.
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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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Take these next steps to learn more about the Google Security Operations Platform. Build on fundamental knowledge to go deeper on customization and tuning.
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In this Google DeepMind course, you will complete a capstone project that brings together the technical knowledge, ethical awareness, and creative problem-solving skills that you have developed throughout the Google DeepMind: AI Research Foundations curriculum. You will apply what you have learned to a problem of your choosing. You will first plan your project to ensure that you are clear on your project aims and intended impact. You will then collect and prepare data for your model. Finally, you will implement a full fine-tuning workflow using Gemma 3 with low-rank adaptation (LoRA) and evaluate its performance. You will receive guidance, but the emphasis will be on adaptation and experimentation - skills that are essential for applied AI research.
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This course brings AI-driven, natural-language data engineering into the IDE and CLI workflows you already use. Built on the Agent Development Kit (ADK), the Data Agent Kit connects to a wide array of Google Cloud services, including BigQuery, Spanner, BigLake, Dataproc, and Managed Airflow. By using it, you can collapse days of pipeline scaffolding, data build tool (dbt) authoring, and orchestration wiring into prompt-driven sessions.
As an intermediate-to-advanced practitioner, you will learn to build a data agent with the Agent Development Kit (ADK). You'll wire up the Model Context Protocol (MCP) layer, and use your agent to explore catalogs, transform data, train models, and orchestrate scheduled pipelines on Google Cloud.
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In this advanced course, you will design, evaluate, deploy, and monitor production-ready AI agents using Customer Experience Agent Studio (CX Agent Studio).
You will apply deterministic design principles, test agents with simulations, and build low-latency voice experiences with Native Audio. You will accelerate development using the CX Agent Studio Model Context Protocol (MCP) server and enterprise connectors. Then, evaluate agent performance with golden test cases and the AI-assisted quality hill climbing process. Finally, deploy your agents across web and telephony channels, configure human escalation, and monitor live conversations using Quality AI in CX Insights.
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As enterprise teams transition to autonomous AI agents, security evolves toward proactive, identity-centric protection. In this course, you will implement a zero-trust security architecture for Cymbal Banking's underwriting platform using the Gemini Enterprise Agent Platform. By deploying a regional Egress Agent Gateway, configuring Model Armor and Semantic Governance (SGP) safety templates, assigning cryptographic SPIFFE identities to agent runtimes, and declaring a VPC-SC perimeter in Terraform, you will protect database boundaries and manage tools with production-grade governance.
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Cloud technology on its own only provides a fraction of the true value to a business; When combined with data–lots and lots of it–it has the power to truly unlock value and create new experiences for customers.
In this course, you'll learn what data is, historical ways companies have used it to make decisions, and why it is so critical for machine learning. This course also introduces learners to technical concepts such as structured and unstructured data. database, data warehouse, and data lakes. It then covers the most common and fastest growing Google Cloud products around data.
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In this Google DeepMind course, you will learn the fundamentals of language models and gain a high-level understanding of the machine learning development pipeline. You will consider the strengths and limitations of traditional n-gram models and advanced transformer models. Practical coding labs will enable you to develop insights into how machine learning models work and how they can be used to generate text and identify patterns in language. Through real-world case studies, you will build an understanding around how research engineers operate. Drawing on these insights you will identify problems that you wish to tackle in your own community and consider how to leverage the power of machine learning responsibly to address these problems within a global and local context.
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In this Google DeepMind course you will learn how to prepare text data for language models to process. You will investigate the tools and techniques used to prepare, structure, and represent text data for language models, with a focus on tokenization and embeddings. You will be encouraged to think critically about the decisions behind data preparation, and what biases within the data may be introduced into models. You will analyze trade-offs, learn how to work with vectors and matrices, how meaning is represented in language models. Finally, you will practice designing a dataset ethically using the Data Cards process, ensuring transparency, accountability, and respect for community values in AI development.
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In this Google DeepMind course you will focus on the training process for machine learning models. You will learn how to spot and mitigate issues when training a model, such as overfitting and underfitting. In practical coding labs, you will implement and evaluate the multilayer perceptron for simple classification tasks. This will provide insights into the mechanics of training a neural network model and the backpropagation algorithm. Research case studies will demonstrate how neural networks power real-world models. Additionally, you will consider the broader social impacts of innovation by looking beyond immediate benefits to anticipate potential risks, safety concerns, and further-reaching societal consequences.
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In this Google DeepMind course you will discover the mechanisms of the transformer architecture. You will investigate how transformer language models process prompts to make context-sensitive next-token predictions. Through practical activities you will explore the attention mechanism, visualize attention weights, and encounter advanced concepts like masked attention and multi-head attention. You will also learn other techniques that are necessary to build neural networks that are well-suited to be used as language models. Finally, through activities on values, stakeholder mapping and community engagement, you will practice concrete tools for ensuring AI projects are developed with communities, not just for them.
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Train more powerful models with a single GPU. In this course, you will learn how hardware can speed up model training and the key considerations when training models on a GPU. First, you will learn how to estimate the number of computations and the amount of computer memory required to train large neural networks. You will then discover techniques for reducing the computing and memory requirements when training a model. Techniques which you will apply for fine-tuning a Gemma model with 4 billion parameters. Finally, you will consider the potential environmental impacts of machine learning, with a focus on where questions of energy, water, and e-waste intersect with justice and equity.
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Unleash the power of language models with fine-tuning. In this course, you will learn how to adjust a pre-trained model to a specific task. You will start with full-parameter fine-tuning using a small language model. To tune larger models like Gemma, you will learn parameter-efficient techniques with a focus on LoRA. Finally, you will be briefly introduced to reinforcement learning as an alternative to supervised fine-tuning (SFT). You will also explore how AI is imagined and made sense of in cultural contexts. You will consider why responsible AI is not just about technical safety but also about building governance systems that reflect community values and protect the public interest.
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您已建構能回覆查詢的基本 LLM 代理，是時候讓代理有狀態。利用工作階段狀態建構能維持對話脈絡、記住使用者偏好，並提供個人化體驗的代理。將代理從無狀態的回覆者轉為智慧助理。
查看 Gemini Enterprise Agent Ready (GEAR) 計畫的其他內容。
加入社群論壇，提出問題並參與討論
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You’ve built capable agents with powerful tools—now ensure they operate safely and reliably. Use callbacks as observation points and control mechanisms. Monitor what’s happening, validate inputs and outputs, implement guardrails, and gain confidence that your agents behave in production.
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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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將代理從 localhost 移至正式環境。本課程將說明如何將 ADK 代理部署至 Vertex AI Agent Engine 和 Cloud Run，並透過 Memory Bank 視情況啟用跨工作階段記憶。
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Learn to coordinate multiple specialized agents working together. This lesson teaches you when to use multi-agent systems, how to orchestrate agents with workflow patterns, and how agents communicate through shared state. By the end, you’ll build a complete multi-agent application.
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充分利用生成式 AI 的强大功能，打造智能的自动化智能体。完成本课程后，您将能够开发数据存储区智能体，该智能体可以自动从您的网站、文档或结构化数据中提取并合成信息，从而即时回答各类复杂问题。告别静态常见问题解答，您的新智能体将提供动态、准确的答案，甚至还会显示原始来源网址，而这一切只需通过简单快速的设置即可实现。
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這堂課程會說明如何依據生成式應對手冊，建構 Conversational Agents 對話服務。您會先認識應對手冊並學習設定方式，接著瞭解測試的重要性，以及在正式環境部署時的考量重點，例如配額限制與整合功能。在課程最後，您會透過個案研究，掌握如何將應對手冊應用於生成式導引。
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這部影片說明如何在 Gemini Canvas 中使用簡單的提示詞，建構、自訂及分享可玩的遊戲，例如井字遊戲。
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這部影片說明如何在 Gemini Canvas 中使用簡單的文字提示詞，打造一台具備自訂控制項和可彈奏鍵盤的全互動式音樂合成器。
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這部影片將說明如何使用 Gemini Deep Research 和創意提示詞，將複雜資訊轉化為有趣的個人化分析，例如運動球探報告和對戰表。
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這部影片將說明如何使用 Gemini Canvas 的「建立」選單，將大篇幅文字立即轉換成資訊圖表和網頁等視覺素材。
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這部影片說明如何使用 Gemini Canvas，透過「直覺式程式開發」技術，將簡單的應用程式繪圖轉化為可運作的原型。
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In this video, you'll learn to use Gemini in Google Sheets to automatically generate organized trackers from unstructured data. You'll learn how to write a prompt that references other Google Drive files using the "@" symbol. You'll also see how Gemini builds a fully formatted, color-coded table with drop-down menus based on the data in your notes, saving you significant manual effort.
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這部影片說明如何使用 Gmail 內建 Gemini 管理 Google 日曆，包括在側邊面板詢問行程，以及一鍵將在電子郵件中偵測到的活動新增至日曆。
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In this video, you'll learn to use Gemini in Google Slides to build polished presentations efficiently. You'll see how to create slides by referencing content directly from your Google Drive files using the "@" symbol. You'll also learn to generate custom, professional images from a simple text prompt to visually enhance your presentation.
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In this video, you'll learn to use the "Ask Gemini" feature in Google Sheets to manage your data with natural language. You'll see how to ask Gemini to create dropdowns, highlight sales data, create a pivot table, filter by month, and sort by revenue.
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In this video, you'll learn to use the =AI() function in Google Sheets to automate your work. You'll see how to generate text like slogans, summarize paragraphs of customer feedback, and categorize data by classifying inquiries and analyzing sentiment.
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這部影片將說明如何運用 Google 試算表中的 Gemini 進階 AI 功能，在幾分鐘內輕鬆提取資料並生成洞察，完全不需要任何技術或程式設計背景。
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這部影片說明如何使用 Gemini 和 Apps Script，在 Google Workspace 中自動執行手動工作。您將學習如何提示 Gemini 生成 Apps Script 程式碼，在 Google 試算表中針對未標示為「完成」的工作，自動草擬電子郵件提醒。即使幾乎沒有技術專業知識，也能自動化工作流程，省下時間處理更重要的工作，不必再手動後續追蹤。
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In this video, you'll learn to use Gemini in Google Sheets for advanced data analysis. You'll learn how to create data visualizations, like a scatter plot, using a simple prompt. You'll also learn how to ask Gemini to analyze your data for strategic insights and recommendations, such as how to save money.
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建立您的第一個 Gemini Enterprise 應用程式，獲得技能徽章！將各種資料來源連接到您的應用程式，建立強大的統合式搜尋和分析引擎。掌握 Deep Research 代理、多代理構思和 NotebookLM 等進階功能，進行重點分析。
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瞭解 AI 代理如何發揮更高的業務影響力，包括根據您的 KPI 規劃要使用的代理類型，以及探索能解決實際瓶頸的用途。您也將認識各種無程式碼到高程式碼解決方案，瞭解 Gemini Enterprise 如何協助建構和自動調度合適的代理。
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本課程介紹 AI 代理的基礎知識，並探討代理的實際應用價值。代理可為開發人員、架構師和技術決策者奠定基礎，協助他們從目標導向的自主行為角度來理解 AI 系統。
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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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