Petr Klimeš
Jest członkiem od 2026
Liga srebrna
11202 pkt.
Security Command Center: Vulnerabilities and Misconfigurations
Earned lis 3, 2026 EST
Google Threat Intelligence
Earned lis 3, 2026 EST
Machine Learning Operations (MLOps) for Generative AI
Earned lis 3, 2026 EST
Introduction Google Security Operations (SOAR)
Earned lis 3, 2026 EST
Introduction to Google Security Operations (SIEM)
Earned lis 3, 2026 EST
Add Agent Capabilities With Tools
Earned lis 3, 2026 EST
Machine Learning Operations (MLOps): Getting Started
Earned lis 3, 2026 EST
Machine Learning Operations (MLOps) with Agent Platform: Model Evaluation
Earned lis 3, 2026 EST
Google Security Operations - Deep Dive
Earned lis 3, 2026 EST
Google DeepMind : 08 Capstone: Develop Your Model for Real-World Impact
Earned lis 3, 2026 EST
Orchestrate Workflows with the Data Agent Kit
Earned lis 3, 2026 EST
Customer Experience Agent Studio: Design and Test Solutions
Earned lis 3, 2026 EST
Google Cloud Agent Governance and Security
Earned lis 3, 2026 EST
Innovating with Data and Google Cloud
Earned lis 3, 2026 EST
Google DeepMind: 01 Build Your Own Small Language Model
Earned lis 3, 2026 EST
Google DeepMind: 02 Represent Your Language Data
Earned lis 3, 2026 EST
Google DeepMind: 03 Design And Train Neural Networks
Earned lis 3, 2026 EST
Google DeepMind: 04 Discover The Transformer Architecture
Earned lis 3, 2026 EST
Google DeepMind: 07 Accelerate Your Model
Earned lis 3, 2026 EST
Google DeepMind: 05 Fine-Tune Your Model
Earned lis 3, 2026 EST
Manage Agent Memory and State
Earned lis 3, 2026 EST
Add Agent Guardrails with Callbacks
Earned lis 3, 2026 EST
Build a Certification Study Guide: PCA Exam Prep
Earned lis 3, 2026 EST
Deploy Your First Agent
Earned lis 3, 2026 EST
Coordinate Multiple Agents
Earned lis 3, 2026 EST
Agent Assist and its Gen AI Capabilities
Earned lis 3, 2026 EST
Create Agents with Generative Playbooks
Earned lis 3, 2026 EST
AI Boost Bites: Create Your Own Retro Arcade Game
Earned paź 16, 2026 EDT
AI Boost Bites: Become a "Vibe DJ"
Earned paź 16, 2026 EDT
AI Boost Bites: Create Your Ultimate College Scouting Report
Earned paź 16, 2026 EDT
AI Boost Bites: One-Click Campaign Visuals
Earned paź 16, 2026 EDT
AI Boost Bites: From Napkin Sketch to Functional App
Earned paź 16, 2026 EDT
AI Boost Bites: Notes to Sheets with Gemini
Earned paź 16, 2026 EDT
AI Boost Bites: Gemini Calendar Hacks in Gmail
Earned paź 16, 2026 EDT
AI Boost Bites: Build Slides Fast with Gemini
Earned paź 16, 2026 EDT
AI Boost Bites: Talk to Your Data in Sheets
Earned paź 16, 2026 EDT
AI Boost Bites: AI Magic in a Sheets Cell
Earned paź 16, 2026 EDT
AI Boost Bites: No-Code Sheets & Scripts
Earned paź 16, 2026 EDT
AI Boost Bites: Automate tasks with Gemini and Apps Script
Earned paź 16, 2026 EDT
AI Boost Bites: Advanced Analysis in Sheets
Earned paź 16, 2026 EDT
Create Your First Gemini Enterprise Application
Earned paź 16, 2026 EDT
Enterprise Agents and Use Cases
Earned paź 16, 2026 EDT
Agent Fundamentals
Earned paź 16, 2026 EDT
Introduction to Generative AI - Polski
Earned paź 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 provides unmatched visibility into threats by delivering detailed and timely threat intelligence to security teams around the world. This course covers the various capabilities of Google Threat Intelligence and common ways that organizations use this product to proactively mitigate threats.
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This course is dedicated to equipping you with the knowledge and tools needed to uncover the unique challenges faced by MLOps teams when deploying and managing Generative AI models, and exploring how Gemini Enterprise Agent Platform empowers AI teams to streamline MLOps processes and achieve success in Generative AI projects.
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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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You’ve built agents with advanced configuration—now give them real-world capabilities. Equip agents with tools that enable searching the web, executing code, querying databases, and performing custom actions. Transform agents from intelligent responders into capable assistants that take action.
Explore other content in the Gemini Agent Ready (GEAR) program.
Join the community forum for questions and discussions
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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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This course equips machine learning practitioners with the essential tools, techniques, and best practices for evaluating both generative and predictive AI models. Model evaluation is a critical discipline for ensuring that ML systems deliver reliable, accurate, and high-performing results in production.
Participants will gain a deep understanding of various evaluation metrics, methodologies, and their appropriate application across different model types and tasks. The course will emphasize the unique challenges posed by generative AI models and provide strategies for tackling them effectively. By leveraging Google Cloud's Agent Platform, participants will learn how to implement robust evaluation processes for model selection, optimization, and continuous monitoring.
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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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You've built basic LLM agents that respond to queries—now let's make them stateful. Use session state to build agents that maintain context, remember user preferences, and provide personalized experiences. Transform agents from stateless responders to intelligent assistants.
Explore other content in the Gemini Agent Ready (GEAR) program.
Join the community forum for questions and discussions
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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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Take your agents from localhost to production. This course teaches you to deploy ADK agents to Agent Runtime and Cloud Run, with optional cross-session memory via Memory Bank.
Explore other content in the Gemini Agent Ready (GEAR) program.
Join the community forum for questions and discussions
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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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Unlock the power of generative AI to create intelligent, automated agents. After completing this course, you'll be equipped to develop a data store agent that can instantly answer complex questions by automatically extracting and synthesizing information from your websites, documents, or structured data. Say goodbye to static FAQs—your new agent will provide dynamic, accurate answers and even surface the original source URLs, all with a simple and rapid setup. Explore other content in the Gemini Agent Ready (GEAR) program.
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This course will teach you how to build conversational experiences for Conversational Agents using Generative Playbooks. You'll start with an introduction to playbooks and learn how to set up your first one. You'll also learn about the importance of testing, as well as key production considerations like quota limits and integration. The course concludes with a case study that shows how to use playbooks for generative steering. Explore other content in the Gemini Agent Ready (GEAR) program.
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This video covers how to use a simple prompt in Gemini Canvas to build, customize, and share your own playable game, like Tic-Tac-Toe.
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In this video, you'll learn to build a working music synthesizer using a simple text prompt in Gemini Canvas. You'll see how to specify controls like waveform, attack, and sustain, and then interact with the generated instrument to create and experiment with your own unique sounds.
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This video covers how to use Gemini Deep Research with creative prompts to turn complex information into a fun, personalized analysis, like a sports scouting report and bracket.
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In this video, you'll learn to use the Create menu in Gemini Canvas to instantly convert a block of text into a variety of visual formats. You’ll see how to take a brainstormed marketing campaign and turn it into both a shareable infographic and an internal webpage with just one click.
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This video covers how to use Gemini Canvas to transform a simple drawing of an app into a functional prototype using a technique called "vibe coding."
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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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This video covers how to use Gemini in Gmail to manage your Google Calendar by asking about your schedule in the side panel and using the one-click "add to calendar" feature for events detected in emails.
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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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This video covers how you can leverage Gemini's advanced AI capabilities within Google Sheets to effortlessly pull data and generate insights in minutes, all without the need for any technical or coding background.
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This video covers how to use Gemini and Apps Script to automate manual tasks across Google Workspace. You'll learn to prompt Gemini to generate Apps Script code that automatically drafts email reminders in Google Sheets for tasks not marked 'Complete.' Automate your workflow with little to no technical expertise, freeing up time for more important work and eliminating manual follow-ups.
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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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Create your first Gemini Enterprise application to earn a skill badge! Connect diverse data sources to your application to build a powerful, unified search and analysis engine. Master advanced capabilities like deep research agents, multi-agent ideation, and Gemini Notebook for focused analysis. Explore other content in the Gemini Agent Ready (GEAR) program.
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Discover how AI agents drive business impact. You’ll map agent types to your KPIs and explore use cases that solve real bottlenecks. Then, learn how Gemini Enterprise empowers you to build and orchestrate the right agents—from no-code to high-code solutions. Explore other content in the Gemini Agent Ready (GEAR) program.
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This course introduces the fundamentals of AI agents and explores where agents add value in the real world. It provides a foundation for developers, architects, and technical decision-makers who want to understand AI systems through the lens of autonomous, goal-directed behavior.
Explore other content in the Gemini Agent Ready (GEAR) program.
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Celem tego szybkiego szkolenia dla początkujących jest wyjaśnienie, czym jest generatywna AI oraz jakie są jej zastosowania. Szkolenie przedstawia również różnice pomiędzy tą technologią a tradycyjnymi systemami uczącymi się, a także narzędzia Google, które pomogą Ci tworzyć własne aplikacje korzystające z generatywnej AI.
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