Google Cloud 運算的基本概念：Cloud 運算基礎知識
Earned Jul 12, 2024 EDT
Google API products: A key to modern application development
Earned Jul 3, 2024 EDT
機器學習簡介：語言處理
Earned Jul 3, 2024 EDT
VM Migration for Partners
Earned Jul 2, 2024 EDT
Managing Change when Moving to Google Cloud
Earned Jul 2, 2024 EDT
Rapid Migration & Modernization Program
Earned Jun 26, 2024 EDT
Managing Security in Google Cloud
Earned Jun 20, 2024 EDT
Preparing for Your Professional Cloud Security Engineer Journey
Earned Jun 11, 2024 EDT
Hybrid Cloud Infrastructure Foundations with Anthos
Earned Jun 5, 2024 EDT
Custom Search with Embeddings in Vertex AI
Earned May 28, 2024 EDT
Introduction to Gemini Enterprise for Customer Experience and Conversational Agents
Earned May 21, 2024 EDT
Developing a Google SRE Culture
Earned May 20, 2024 EDT
使用 Gemini：端對端 SDLC
Earned May 12, 2024 EDT
Improving developer velocity with Gemini Code Assist
Earned May 10, 2024 EDT
使用 Gemini：數據資料學家和分析師
Earned May 3, 2024 EDT
使用 Gemini：資安工程師
Earned May 2, 2024 EDT
使用 Gemini：DevOps 工程師
Earned Apr 20, 2024 EDT
使用 Gemini：網路工程師
Earned Apr 19, 2024 EDT
使用 Gemini：應用程式開發人員
Earned Apr 18, 2024 EDT
使用 Gemini：雲端架構師
Earned Apr 15, 2024 EDT
Building Gen AI Apps with Vertex AI: Prompting and Tuning
Earned Apr 13, 2024 EDT
Modernizing Mainframe Applications with Google Cloud
Earned Apr 8, 2024 EDT
Search with AI Applications
Earned Mar 25, 2024 EDT
Vector Search 和嵌入
Earned Mar 18, 2024 EDT
Serverless Data Processing with Dataflow: Foundations
Earned Feb 24, 2024 EST
Building Resilient Streaming Systems on Google Cloud Platform
Earned Feb 17, 2024 EST
Smart Analytics, Machine Learning, and AI on Google Cloud
Earned Feb 17, 2024 EST
Build Batch Data Pipelines on Google Cloud
Earned Jan 31, 2024 EST
Build Data Lakes and Data Warehouses on Google Cloud
Earned Jan 22, 2024 EST
Preparing for your Professional Data Engineer Journey
Earned Jan 11, 2024 EST
Conversational AI on Vertex AI and Dialogflow CX
Earned Dec 9, 2023 EST
開始使用 Google Kubernetes Engine
Earned Nov 26, 2023 EST
彈性的 Google Cloud 基礎架構：資源調度與自動化
Earned Nov 24, 2023 EST
Preparing for your Professional Cloud Architect Journey
Earned Nov 14, 2023 EST
ML Pipelines on Google Cloud
Earned Oct 24, 2023 EDT
Machine Learning Operations (MLOps): Getting Started
Earned Oct 18, 2023 EDT
Recommendation Systems on Google Cloud
Earned Oct 17, 2023 EDT
Natural Language Processing on Google Cloud
Earned Oct 11, 2023 EDT
Computer Vision Fundamentals with Google Cloud
Earned Oct 7, 2023 EDT
Production Machine Learning Systems
Earned Oct 1, 2023 EDT
Machine Learning in the Enterprise
Earned Sep 28, 2023 EDT
Feature Engineering
Earned Sep 19, 2023 EDT
Build, Train and Deploy ML Models with Keras on Google Cloud
Earned Sep 16, 2023 EDT
Launching into Machine Learning
Earned Sep 14, 2023 EDT
How Google Does Machine Learning
Earned Sep 8, 2023 EDT
Google Cloud Big Data and Machine Learning Fundamentals
Earned Sep 5, 2023 EDT
Google Cloud 的 AI 和機器學習服務簡介
Earned Sep 1, 2023 EDT
Generative AI for Business Leaders
Earned Aug 28, 2023 EDT
Text Prompt Engineering Techniques
Earned Aug 26, 2023 EDT
Implementing Generative AI with Vertex AI
Earned Aug 24, 2023 EDT
Vertex AI Studio 簡介
Earned Aug 23, 2023 EDT
建立圖像說明生成模型
Earned Aug 22, 2023 EDT
Transformer 和 BERT 模型
Earned Aug 22, 2023 EDT
編碼器-解碼器架構
Earned Aug 22, 2023 EDT
注意力機制
Earned Aug 21, 2023 EDT
Generative AI Explorer : Vertex AI
Earned Jul 30, 2023 EDT
圖像生成簡介
Earned Jul 29, 2023 EDT
負責任的 AI 技術：透過 Google Cloud 採用 AI 開發原則
Earned Jul 29, 2023 EDT
Generative AI Fundamentals
Earned Jul 28, 2023 EDT
負責任的 AI 技術簡介
Earned Jul 28, 2023 EDT
大型語言模型簡介
Earned Jul 28, 2023 EDT
生成式 AI 簡介
Earned Jul 27, 2023 EDT
Architecting with Google Kubernetes Engine: Workloads
Earned Oct 14, 2021 EDT
Architecting with Google Kubernetes Engine: Foundations - 繁體中文
Earned Oct 5, 2021 EDT
可靠的 Google Cloud 基礎架構：設計與程序
Earned Sep 22, 2021 EDT
Elastic Google Cloud Infrastructure: Scaling and Automation - Locales
Earned Sep 20, 2021 EDT
This course educates partners on key concepts of Google’s Migrate to Containers. It will cover planning, workload fitness for conversion, deployment with a processing cluster, and the migration process.
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Perform a migration from Oracle to BigQuery using SQL Translation and DataFlow using Sample Data. Learners will complete a quiz that focuses on the process of transferring both schema and data from an Oracle enterprise data warehouse to BigQuery.
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Migration from Oracle to Cloud Spanner using HarbourBridge. This course describes an example scenario that uses sample data during the migration. This process includes using HarbourBridge for Assessment, Schema Conversion, Schema Transformation, Data Migration, and supporting tools for data validation.
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Planning for a Google Workspace Deployment is the final course in the Google Workspace Administration series.
In this course, you will be introduced to Google's deployment methodology and best practices. You will follow Katelyn and Marcus as they plan for a Google Workspace deployment at Cymbal. They'll focus on the core technical project areas of provisioning, mail flow, data migration, and coexistence, and will consider the best deployment strategy for each area.
You will also be introduced to the importance of Change Management in a Google Workspace deployment, ensuring that users make a smooth transition to Google Workspace and gain the benefits of work transformation through communications, support, and training.
This course covers theoretical topics, and does not have any hands on exercises. If you haven’t already done so, please cancel your Google Workspace trial now to avoid any unwanted charges.
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The goals at the end of this course are to be able to articulate to customers when and why they should use Looker’s multistage development framework and to share high-level ways to promote LookML code and content across multiple Looker instances.
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Many traditional enterprises use legacy systems and applications that can't stay up-to-date with modern customer expectations. Business leaders often have to choose between maintaining their aging IT systems or investing in new products and services. "Modernize Infrastructure and Applications with Google Cloud" explores these challenges and offers solutions to overcome them by using cloud technology.
Part of the Cloud Digital Leader learning path, this course aims to help individuals grow in their role and build the future of their business.
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The fastest way to improve machine learning outcomes is to focus on your data. In this course you'll review the common challenges with data in ML, and then learn how to overcome these challenges using Vertex AI Feature Stores.
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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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What is cloud technology or data science? More importantly, what can it do for you, your team, and your business?
If you want to learn about cloud technology so you can excel in your role and help build the future of your business, then this introductory course on digital transformation is for you.
This course defines foundational terms such as cloud, data, and digital transformation. It also explores examples of companies around the world that are using cloud technology to revolutionize their businesses. The course provides an overview of the types of opportunities and challenges that companies often encounter in their digital transformation journey and aligns them with the Google Cloud solution pillars. But digital transformation isn't just about using new technology. To truly transform, organizations also need to be innovative and scale an innovation mindset across the organization. The course offers best practices to help you achieve this.
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Explore how to use AI to automate document processing tasks, such as classifying documents, extracting data from documents, and summarizing documents. Learn how to use the Document AI Workbench to create custom document extractors and summarizers. Upload documents, define fields, create versions, and call endpoints to get structured data and summaries back. Discover a new service called Document AI Warehouse, which is a fully managed service to search, store, govern, and manage documents and their extracted metadata. You will also learn about how it integrates with other Google Cloud services like Document AI, BigQuery, and Cloud Storage.
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This course focuses on modernizing applications using OpenShift on Google Cloud. Throughout this course, you'll gain the skills necessary to describe and understand OpenShift and successfully re-platform it to Google Cloud.
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Learn about new generative AI features in App Development, including Duet AI for VS Code, Cloud Workstations and Colab Enterprise, as well as application prototyping using natural language prompts in AppSheet.
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Learn about Generative AI, Vectors and Applications, including vector embedding in PostgreSQL, Cloud SQL for PostgreSQL and the pgvector extension. As well as building Generative AI powered apps faster with Duet AI.
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This course discusses the key elements of Google's Data Warehouse solution portfolio and strategy.
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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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Migration from on-premises VMware to Google Cloud Compute Engine using Migrate to Virtual Machines (v5) using demo VM(s). It provides a proof-of-concept that walks you through the process of replicating a VM to doing test cutover and final cutover of the VM.
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Want to learn more about Google Cloud?
Grow your Google Cloud knowledge, strengthen your skills to win with customers, and scale your Google Cloud business. Find it here in one handy location.
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Want to learn more about Google Cloud?
Grow your Google Cloud knowledge, strengthen your skills to win with customers, and scale your Google Cloud business. Find it here in one handy location.
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Learn about the new skills you'll need to be successful when using generative AI. Google Cloud has used generative AI to help keep you engaged and streamline your learning journey.
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Explore the four pillars of Enterprise Readiness in generative AI: data governance and privacy, security and compliance support, infrastructure reliability and sustainability, and responsible AI. You will also learn how these pillars address concerns about data privacy and security. Learn about customizing foundation models with your data while keeping your data safe using adapter layers, how to keep your AI models safe and compliant when deploying them across the world, and the multiple layers of encryption, rigorous controls, supply chain audits, and ongoing security testing that are built into Google Cloud. You will also learn about security controls such as VPC, customer-managed encryption keys, access transparency, and data residency zones. And explore enterprise controls, certifications, and responsible AI tooling available in Vertex AI to ensure your data remains secure and compliant with global regulations when deploying generative AI models.
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This workload aims to upskill Google Cloud partners to perform specific tasks associated with priority workloads. Learners will perform the tasks for migrating data from AWS Redshift to BigQuery using BigQuery Data Transfer Service, which includes sample mock data. Learners will complete a challenge lab that focuses on the process of transferring both schema and data from a Redshift data warehouse to BigQuery.
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The Database Summit learning path is a curated collection of courses and quests that provide converage of infrastructure, database migration, and SQL operations.
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The Cloud Foundations Customer Onboarding: Best Practices course enables partners to onboard customers on Google Cloud efficiently and in minimum time, by imparting knowledge, IP, and best practices from the Technical Onboarding Center (TOC) team at Global Delivery Center (GDC). The course explores Cloud Identity and organization, users and groups, administrative access, and resource hierarchy. It also examines network configuration, hybrid connectivity, logging and monitoring, and organizational security.
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RHLF is a technique for fine-tuning language models by incorporating human feedback into the training process. This course explores how you can use RHLF to improve the performance of language models on various tasks, such as text summarization and question answering.
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In this course, you will learn about the Apigee Integration solution and its architecture. You will learn how to identify and develop customer opportunities while differentiating Google's offering from other competitors. Also, the course includes a deep dive into the use of Connectors in Apigee Integrations, as well as demos into how the implementation configurations for design, deployment, monitoring and debugging are carried out.
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This course provides an overview of Network Monitoring and Troubleshooting on Google Cloud.
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This course provided technical training in Google Cloud Dataflow, the foundational pillar of Google Cloud's streaming analytics solution. This training is intended for Google Cloud technical experts that are looking to further their understanding of Dataflow to advance sales-related technical evaluations, customer implementations, technical support, and data processing applications. This course explores topics related to Dataflow, including: Apache Beam SDK Google Cloud Dataflow Runner Autoscaling Logic Sources / Sinks Schemas / Dataflow SQL Dynamic Work RebalancingMonitoring, Troubleshooting, and Optimization Testing and CI/CD
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In this course, you will learn about GDC air-gapped (previously known as GDC Hosted), an offering from Google Distributed Cloud. This course provides both a business and technical overview of GDC air-gapped, exploring its key features and target customers.
Participants will gain insights into GDC air-gapped's value proposition and learn how to effectively communicate its benefits to potential clients, enabling them to qualify for sales opportunities.
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As organizations move their data and applications to the cloud, they must address new security challenges. The Trust and Security with Google Cloud course explores the basics of cloud security, the value of Google Cloud's multilayered approach to infrastructure security, and how Google earns and maintains customer trust in the cloud.
Part of the Cloud Digital Leader learning path, this course aims to help individuals grow in their role and build the future of their business.
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This course further explores SQL Server on Google Cloud.
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Certificate Authority Service is a highly-available, scalable Google Cloud service. This course covers how Certificate Authority Service enables IT and security teams to simplify and automate the deployment, management, and security of private certificate authorities (CA) while staying in control of their private keys.
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This course focuses on how you can leverage the Google Cloud Analytics and AI/ML offerings to integrate and innovate with SAP
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This course gives you a deep dive into the workflows of Tier 3 analysts.
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In this course you will discover additional tools for your toolbox for working with complex deployments, building robust solutions, and delivering even more value.
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Hands on course covering the main uses of extends and the three primary LookML objects extends are used on as well as some advanced usage of extends.
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This course reviews the processes for creating table calculations, pivots and visualizations
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This course has been updated, please enroll in the new
Elastic Google Cloud Infrastructure: Scaling and Automation.
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The first course provides a high-level overview of security fundamentals on the GDC platform.
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The course explores advanced services such as machine learning, and operational topics such as application deployment, monitoring, and troubleshooting.
In addition, we’ll introduce GDC software upgrades, logging, billing, and cost monitoring.
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The course examines service resources or workload components that exist in projects. You’ll learn about Kubernetes in GDC, Artifact Registry, GDC Object Storage, Database Service, Networking, and Key Management and Security.
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This L300 course explores the intricacies of the hardware and networking infrastructure, examines the role of Kubernetes in container orchestration, and how to master the deployment process. The course emphasizes critical security aspects, guiding you through defense-in-depth design, zero-trust architecture, and essential operational security measures for protecting sensitive data. You'll also gain valuable insights into operational aspects, such as resource management, upgrades, and solutions tailored for GDC customers.
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This course provides an introduction to the GDC platform—which enables you to host, control, and manage infrastructure and services directly on your premises.
GDC air-gapped is one component of Google Distributed Cloud offering which aligns to Google’s digital sovereignty vision. It supports public-sector customers and commercial entities that have strict data residency, security or privacy requirements.
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This L200 course comprehensively explores GDC air-gapped's concepts, architecture, and operational aspects, equipping learners with the knowledge to deploy and manage this solution effectively. The course delves into topics such as the roles of vendors and partners, hardware and software components, zero trust security, multi-tenancy, support and operations, observability, Identity and Access Management, managed services, and the GDC Sandbox environment. Furthermore, the course provides insights into compliance and accreditation processes, ensuring learners understand the regulatory landscape and can navigate it successfully. By the end of this course, learners will have a solid understanding of GDC air-gapped and be prepared to leverage its capabilities for their organization's needs.
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This L200 course comprehensively explores GDC connected concepts, architecture, and operational aspects, equipping learners with the knowledge to deploy and manage this solution effectively. The course delves into topics such as its survivability features and best practices, security, networking, software stack, and hardware options. Furthermore, the course provides insights into its operating model. By the end of this course, learners will have a solid understanding of GDC connected and be prepared to leverage its capabilities for their organization's needs.
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Artificial intelligence (AI) and machine learning (ML) represent an important evolution in information technologies that are quickly transforming a wide range of industries. “Innovating with Google Cloud Artificial Intelligence” explores how organizations can use AI and ML to transform their business processes.
Part of the Cloud Digital Leader learning path, this course aims to help individuals grow in their role and build the future of their business.
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Explore Generative AI in API management and Application Integration, including Duet AI in Apigee, and extensions for Vertex AI. Discover the new opportunities with generative AI, including conversational APIs, Auto-Operators, and API growth. Use Duet AI to create an API specification in-context, and use Duet AI to create an integration. Use Duet AI in Apigee API Hub, and create an LLM extension.
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Gemini 版 Google Workspace 是一項外掛程式，可讓客戶在 Google Workspace 使用生成式 AI 功能。這堂迷你課程會介紹 Gemini 的主要功能，並說明如何在 Google 簡報善用這些功能，提高生產力和效率。
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Discover how to use Colab Enterprise, a managed notebook environment that provides secure and compliant storage for your notebooks, that comes with two code-generation features: code complete and code gen. Create and use runtime templates in Vertex AI Workbench to give users access to more powerful compute resources while still maintaining control over the types of resources that are spun up. Share notebooks with other users and use versioning to keep track of changes to your notebooks. Learn how Colab Enterprise integrates BigQuery and Vertex AI. You will see how to pull data from BigQuery, use BQML to train a model, and have it all integrated with Vertex Model Registry. Explore how to fine-tune a Foundation model or generative AI model using the Vertex AI SDK. And, learn how to evaluate a tuned model and compare the results of multiple runs.
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DORA (DevOps Research & Assessment) is a research program, an assessment tool, a report publisher, and more. Together, these products create a compelling customer story that defines the industry standard for successful DevOps and technology transformation, and provides personalized steps to accelerate the customer journey. DORA enables Googlers and Partners to bring DevOps research and practices to Google Cloud Customers. This course provides an introduction to DORA and a guide on how to successfully complete a DORA assessment for your customer. Engaging customers in DORA assessment provides invaluable insights into the customer’s organization, and helps you better support your customer. The DORA training was originally designed for and only made available to Google Teams, however we’ve recognized how beneficial it would be for our Partners and are now offering our Partners exclusive access to the DORA training and products, so they can benefit from DORA’s research and practices …
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Not all ML workloads benefit from hardware acceleration, but when they do, Google Cloud has you covered. Learn when and how to use GPU and TPU accelerators most effectively in your ML workloads on Google Cloud.
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Model experimentation and evaluation are critical steps in the journey to productionalize an LLM. This course introduces new tools that will help simplify these tasks.
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An introduction to Large models, Cloud TPUs and GKE. 15 step training for how to get started with Cloud TPUs and GKE, and explore training jobs, example workloads and inference with TPUs on GKE. Discover an app using personalized generative AI.
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Google Cloud : Prompt Engineering Guide examines generative AI tools, how they work. We'll explore how to combine Google Cloud knowledge with prompt engineering to improve Gemini responses.
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Learn how to use NotebookLM to create a personalized study guide for the Professional Machine Learning Engineer certification exam (PMLE). You'll review NotebookLM features, create a notebook, and use the study guide to practice for a certification exam.
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Artificial Intelligence (AI) offers transformative possibilities, but it also introduces new security challenges. This course equips security and data protection leaders with strategies to securely manage AI within their organizations. Learn a framework for proactively identifying and mitigating AI-specific risks, protecting sensitive data, ensuring compliance, and building a resilient AI infrastructure. Pick use cases from four different industries to explore how these strategies apply in real-world scenarios.
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This course introduces you to the world of reliable deep learning, a critical discipline focused on developing machine learning models that not only make accurate predictions but also understand and communicate their own uncertainty. You'll learn how to create AI systems that are trustworthy, robust, and adaptable, particularly in high-stakes scenarios where errors can have significant consequences.
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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 Vertex AI Feature Store's streaming ingestion at the SDK layer.
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In the last installment of the Dataflow course series, we will introduce the components of the Dataflow operational model. We will examine tools and techniques for troubleshooting and optimizing pipeline performance. We will then review testing, deployment, and reliability best practices for Dataflow pipelines. We will conclude with a review of Templates, which makes it easy to scale Dataflow pipelines to organizations with hundreds of users. These lessons will help ensure that your data platform is stable and resilient to unanticipated circumstances.
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Welcome to the third course of the "Networking in Google Cloud" series: Network Architecture!
In this course, you will explore the fundamentals of designing efficient and scalable network architectures within Google Cloud.
In the first module, Introduction to Network Architecture, we'll start by introducing you to the core components and concepts of network architecture, including subnets, routes, firewalls, and load balancing.
Then in the second module, network topologies, we'll dive into various network topologies commonly used in Google Cloud, discussing their strengths, and weaknesses.
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Welcome to the second course in the networking and Google Cloud series routing and addressing.
In this course, we'll cover the central routing and addressing concepts that are relevant to Google Cloud's networking capabilities.
Module one will lay the foundation by exploring network routing and addressing in Google Cloud, covering key building blocks such as routing IPv4, bringing your own IP addresses and setting up cloud DNS. In Module two will shift our focus to private connection options, exploring use cases and methods for accessing Google and other services privately using internal IP addresses.
By the end of this course, you'll have a solid grasp of how to effectively route and address your network traffic within Google Cloud.
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Google Cloud 運算基本概念課程，適合幾乎沒有雲端運算背景或經驗的學員。這些課程會概略介紹雲端基礎知識、大數據和機器學習的核心概念，以及 Google Cloud 的角色和定位。完成這一系列課程後，學員將能闡述這些概念並展示實用技能。學員需依序完成課程：
1. Google Cloud 運算的基本概念：Cloud 運算基礎知識
2. Google Cloud 運算的基本概念：Google Cloud 基礎架構
3. Google Cloud 運算的基本概念：Google Cloud 的網路與安全性
4. Google Cloud 運算的基本概念：Google Cloud 中的資料、機器學習和 AI
本系列的最後一堂課回顧了代管大數據服務、機器學習與這項技術的價值，以及如何獲得技能徽章，進一步展示您的 Google Cloud 技能。
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Google Cloud 運算基本概念課程，適合幾乎沒有雲端運算背景或經驗的學員。這些課程會概略介紹雲端基礎知識、大數據和機器學習的核心概念，以及 Google Cloud 的角色和定位。完成這一系列課程後，學員將能闡述這些概念並展示實用技能。學員需依序完成課程：
1. Google Cloud 運算的基本概念：Cloud 運算基礎知識
2. Google Cloud 運算的基本概念：Google Cloud 基礎架構
3. Google Cloud 運算的基本概念：Google Cloud 的網路與安全性
4. Google Cloud 運算的基本概念：Google Cloud 中的資料、機器學習和 AI
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這堂初級課程將介紹 Google Cloud 的資料分析工作流程，以及用於探索、分析資料並以圖表呈現的工具。您也能學會如何與相關人員分享自己的發現結果。本課程包含個案研究、實作實驗室、講座、測驗和示範，實際展示如何將原始資料集轉化為清晰的資料，進而呈現出能發揮成效的圖表和資訊主頁。無論您是資料領域從業人員、想瞭解如何透過 Google Cloud 取得成功，或有意在職涯中更上一層樓，本課程都能協助您踏出第一步。絕大多數在工作上執行或運用資料分析的學員，都能從本課程受益。
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Enterprises of all sizes have trouble making their information readily accessible to employees and customers alike. Internal documentation is frequently scattered across wikis, file shares, and databases. Similarly, consumer-facing sites often offer a vast selection of products, services, and information, but customers are frustrated by ineffective site search and navigation capabilities.
This course teaches you to use Generative AI App Builder to integrate enterprise-grade generative AI search.
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Get hands-on experience applying and building rules for Chronicle. You learn what YARA-L is and how to customize & create event rules.
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This course helps you understand how to use Chronicle to properly handle security incidents.
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Learn the technical aspects you need to know about Chronicle and how it can help you detect and action threats.
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This course helps developers customize Chronicle and augment its abilities with third party integrations.
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This course is for Partner sellers and technical pre-sales engineers to gain a comprehensive understanding of Google Cloud's cutting-edge Generative AI capabilities and learn to identify high-impact use cases.
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This course is for Partner sellers and technical pre-sales engineers to gain a comprehensive understanding of Google Cloud's cutting-edge Generative AI capabilities, learn to identify high-impact use cases, and develop the skills to demonstrate and integrate these technologies seamlessly into client solutions and operations.
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This course is for Google Cloud’s top partner sellers and technical pre-sales engineers to gain a comprehensive understanding of Google Cloud's cutting-edge Generative AI capabilities and learn to identify high-impact use cases. Those who complete the training and assessment will receive the Google Cloud Generative AI Trailblazer badge through Skills Boost.
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Take the next steps in working with the Chronicle Security Operations Platform. Build on fundamental knowledge to go deeper on cusotmization and tuning.
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This course covers the baseline skills needed for the Google Security Operations Platform. The modules will cover specific actions and features that security engineers should become familiar with to start using the toolset.
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This course will familiarize you with the core functionality of Chronicle, including the user interface, connections, and settings.
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Learn which Mandiant products directly enhance or augment capabilities provided by Chronicle SIEM and SOAR and how those products integrate into our workflow.
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This course will provide you with an overview of SIEM technology to set the stage for the differentiation and expansion of capabilities that Chronicle SIEM provides.
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Google Cloud 運算基本概念課程，適合幾乎沒有雲端運算背景或經驗的學員。這些課程會說明雲端運算基本知識、大數據和機器學習的核心概念，以及 Google Cloud 的角色和定位。完成這一系列課程後，學員將能夠闡述這些概念並展示實用技能。學員應依以下順序完成課程：
1. Google Cloud 運算的基本概念：Cloud 運算基礎知識
2. Google Cloud 運算的基本概念：Google Cloud 基礎架構
3. Google Cloud 運算的基本概念：Google Cloud 的網路與安全性
4. Google Cloud 運算的基本概念：Google Cloud 中的資料、機器學習和 AI
第一門課會概略說明雲端運算、Google Cloud 的使用方式，以及不同的運算選項。
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Outline the key steps in publishing an API to deliver selective company information to applications created by external developers.
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大家都知道，機器學習是發展最快的科技領域之一， 而 Google Cloud Platform 在這方面功不可沒。 GCP 提供多種 API，凡是與機器學習相關的任務，幾乎都能處理。您將在本入門課程的 實驗室，實際演練機器學習技術 在語言處理方面的應用，學會如何從文中擷取實體資訊、 執行情緒和語法分析，並使用 Speech-to-Text API 轉錄語音。
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This course provides comprehensive skills on VM migration, from the initial assessment through the final implementation through presentations, demonstrations, and whiteboard session.
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Moving to the cloud creates numerous opportunities to start working in
a new way and it empowers the workforce to better collaborate and innovate. But
it’s also a big change. Sometimes the success of the change hinges not on the change
itself, but on how it’s managed.
This course will help people managers to understand
some of the key challenges associated with cloud adoption, and provide them with
a verified in-the-field framework that will assist them in supporting their teams
on the change journey. By addressing the human factor of moving to the cloud, organizations
increase their chances of realizing business objectives and investing in their future
talent.
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The Google Cloud Rapid Migration & Modernization Program (RaMP) is a holistic, end-to-end migration/modernization program that helps customers & partners leverage expertise and best practices, lower risk, control costs, and simplify a customer's path to cloud success. This course will give an overview of the program and some of the tools and best practices available to support customer migrations & modernizations.
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This self-paced training course gives participants broad study of security controls and techniques on Google Cloud. Through recorded lectures, demonstrations, and hands-on labs, participants explore and deploy the components of a secure Google Cloud solution, including Cloud Identity, Resource Manager, IAM, Virtual Private Cloud firewalls, Cloud Load Balancing, Cloud Peering, Cloud Interconnect, and VPC Service Controls.
This is the first course of the Security in Google Cloud series. After completing this course, enroll in the Security Best Practices in Google Cloud course.
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This course helps learners prepare for the Professional Cloud Security Engineer (PCSE) Certification exam. Learners will be exposed to and engage with exam topics through a series of lectures, diagnostic questions, and knowledge checks. After completing this course, learners will have a personalized workbook that will guide them through the rest of their certification readiness journey.
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Welcome to Hybrid Cloud Infrastructure Foundations with Anthos!
This is the first course of the Architecting Hybrid Cloud Infrastructure with Anthos path. Anthos enables you to build and manage modern applications, and gives you the freedom to choose where to run them. Anthos gives you one consistent experience in both your on-premises and cloud environments.
During this course, you will be presented with modules that will take you through skills that you will use as an architect or administrator running Anthos environments. The modules in this course include videos, hands-on labs, and links to helpful documentation.
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This course explores Google Cloud technologies to create and generate embeddings. Embeddings are numerical representations of text, images, video and audio, and play a pivotal role in many tasks that involve the identification of similar items, like Google searches, online shopping recommendations, and personalized music suggestions. Specifically, you’ll use embeddings for tasks like classification, outlier detection, clustering and semantic search.
You’ll combine semantic search with the text generation capabilities of an LLM to build Retrieval Augmented Generation (RAG) systems and question-answering solutions, on your own proprietary data using Google Cloud’s Vertex AI.
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This course explores the different products and capabilities of Gemini Enterprise for Customer Experience and Conversational Agents. Additionally, it covers the foundational principles of conversation design to craft engaging and effective experiences that emulate human-like experiences specific to the Chat channel.
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In many IT organizations, incentives are not aligned between developers, who strive for agility, and operators, who focus on stability. Site reliability engineering, or SRE, is how Google aligns incentives between development and operations and does mission-critical production support. Adoption of SRE cultural and technical practices can help improve collaboration between the business and IT. This course introduces key practices of Google SRE and the important role IT and business leaders play in the success of SRE organizational adoption.
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本課程介紹的 Gemini 是採用生成式 AI 技術的協作工具，可協助您透過 Google Cloud 使用 Google 產品和服務，開發、測試、部署及管理應用程式。有了 Gemini 的協助，您會學到如何開發和建構網頁應用程式、修正應用程式中的錯誤、開發測試及查詢資料。在實作研究室中，您也會體驗到 Gemini 如何改良軟體開發生命週期 (SDLC)。
Duet AI 已更名為 Gemini，這是我們的新一代模型。
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Learn how Gemini can revolutionize your ability to develop applications! This course helps developers go beyond the basics and learn how to integrate Gemini into their workflows.
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本課程介紹的 Gemini 是採用生成式 AI 技術的協作工具，可協助分析客戶資料及預測產品銷售情形。您也會學習如何在 BigQuery 中使用客戶資料識別、分類及開發新客戶。透過使用實作研究室，您可以體驗 Gemini 如何改良資料分析和機器學習工作流程。
Duet AI 已更名為 Gemini，這是我們的新一代模型。
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本課程介紹的 Gemini 是採用生成式 AI 技術的協作工具，可協助您透過 Google Cloud 保護雲端環境和資源。您將學到如何將工作負載範例部署到 Google Cloud 中的環境，以及運用 Gemini 找出並修復安全性設定錯誤。在實作研究室中，您也會體驗到 Gemini 如何改良雲端安全防護機制。
Duet AI 已更名為 Gemini，這是我們的新一代模型。
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本課程介紹的 Gemini 是採用生成式 AI 技術的協作工具，可協助工程師透過 Google Cloud 管理基礎架構。您將學到如何透過提示讓 Gemini 尋找和瞭解應用程式記錄檔、建立 GKE 叢集，以及研究如何打造建構環境。在實作研究室中，您也會瞭解 Gemini 如何改良開發運作的工作流程。
Duet AI 已更名為 Gemini，是我們新一代的模型。
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本課程介紹的 Gemini 是採用生成式 AI 技術的協作工具，可協助網路工程師建立、更新及維護虛擬私有雲網路。您將瞭解如何透過提示讓 Gemini 為網路工作提供指引，獲得比搜尋結果更具體的資訊。在實作研究室中，您也會體驗到 Gemini 如何簡化 Google Cloud 虛擬私有雲網路的作業。
Duet AI 已更名為 Gemini，這是我們的新一代模型。
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本課程介紹的 Gemini 是採用生成式 AI 技術的協作工具，可協助開發人員透過 Google Cloud 建構應用程式。您將瞭解如何透過提示讓 Gemini 為您解釋程式碼內容、推薦 Google Cloud 服務，以及生成應用程式的程式碼。在實作研究室中，您也會體驗到 Gemini 如何改良應用程式的開發工作流程。
Duet AI 已更名為 Gemini，這是我們的新一代模型。
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本課程介紹的 Gemini 是採用生成式 AI 技術的協作工具，可協助管理員在 Google Cloud 佈建基礎架構。您將瞭解如何透過提示讓 Gemini 解釋基礎架構、部署 GKE 叢集，以及更新既有的基礎架構。在實作研究室中，您也會體驗到 Gemini 如何改良 GKE 的部署工作流程。
Duet AI 已更名為 Gemini，這是我們的新一代模型。
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(This course was previously named Multimodal Prompt Engineering with Gemini and PaLM) This course teaches how to use Vertex AI Studio, a Google Cloud console tool for rapidly prototyping and testing generative AI models. You learn to test sample prompts, design your own prompts, and customize foundation models to handle tasks that meet your application's needs. Whether you are looking for text, chat, code, image or speech generative experiences Vertex AI Studio offers you an interface to work with and APIs to integrate your production application.
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This course enables system integrators and partners to understand the principles of automated migrations, plan legacy system migrations to Google Cloud leveraging G4 Platform, and execute a trial code conversion.
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(Previously named "Developing apps with Vertex AI Agent Builder: Search". Please note there maybe instances in this course where previous product names and titles are used)
Enterprises of all sizes have trouble making their information readily accessible to employees and customers alike. Internal documentation is frequently scattered across wikis, file shares, and databases. Similarly, consumer-facing sites often offer a vast selection of products, services, and information, but customers are frustrated by ineffective site search and navigation capabilities.
This course teaches you to use AI Applications to integrate enterprise-grade generative AI search.
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這堂課程會介紹 AI 搜尋技術、工具和應用程式。主題涵蓋使用向量嵌入執行語意搜尋；結合語意和關鍵字做法的混合型搜尋機制；以及運用檢索增強生成 (RAG) 技術建構有基準的 AI 代理，盡可能減少 AI 幻覺。您可以實際使用 Vertex AI Vector Search，打造智慧型搜尋引擎。
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This course is part 1 of a 3-course series on Serverless Data Processing with Dataflow. In this first course, we start with a refresher of what Apache Beam is and its relationship with Dataflow. Next, we talk about the Apache Beam vision and the benefits of the Beam Portability framework. The Beam Portability framework achieves the vision that a developer can use their favorite programming language with their preferred execution backend. We then show you how Dataflow allows you to separate compute and storage while saving money, and how identity, access, and management tools interact with your Dataflow pipelines. Lastly, we look at how to implement the right security model for your use case on Dataflow.
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This 1-week, accelerated on-demand course builds upon Google Cloud Platform Big Data and Machine Learning Fundamentals. Through a combination of video lectures, demonstrations, and hands-on labs, you'll learn to build streaming data pipelines using Google cloud Pub/Sub and Dataflow to enable real-time decision making. You will also learn how to build dashboards to render tailored output for various stakeholder audiences.
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Incorporating machine learning into data pipelines increases the ability to extract insights from data. This course covers ways machine learning can be included in data pipelines on Google Cloud. For little to no customization, this course covers AutoML. For more tailored machine learning capabilities, this course introduces Notebooks and BigQuery machine learning (BigQuery ML). Also, this course covers how to productionalize machine learning solutions by using Vertex AI.
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In this intermediate course, you will learn to design, build, and optimize robust batch data pipelines on Google Cloud. Moving beyond fundamental data handling, you will explore large-scale data transformations and efficient workflow orchestration, essential for timely business intelligence and critical reporting.
Get hands-on practice using Dataflow for Apache Beam and Serverless for Apache Spark (Dataproc Serverless) for implementation, and tackle crucial considerations for data quality, monitoring, and alerting to ensure pipeline reliability and operational excellence. A basic knowledge of data warehousing, ETL/ELT, SQL, Python, and Google Cloud concepts is recommended.
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While the traditional approaches of using data lakes and data warehouses can be effective, they have shortcomings, particularly in large enterprise environments. This course introduces the concept of a data lakehouse and the Google Cloud products used to create one. A lakehouse architecture uses open-standard data sources and combines the best features of data lakes and data warehouses, which addresses many of their shortcomings.
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This course helps learners create a study plan for the PDE (Professional Data Engineer) certification exam. Learners explore the breadth and scope of the domains covered in the exam. Learners assess their exam readiness and create their individual study plan.
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In this course you will learn how to use the new generative AI features in Dialogflow CX to create virtual agents that can have more natural and engaging conversations with customers. Discover how to deploy generative fallback responses to gracefully handle errors and omissions in customer conversations, deploy generators to increase intent coverage, and structure, ingest, and manage data in a data store. And explore how to deploy and maintain generative AI agents using your data, and deploy and maintain hybrid agents in combination with existing intent-based design paradigms.
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歡迎參加「開始使用 Google Kubernetes Engine」課程。Kubernetes 是位於應用程式和硬體基礎架構之間的軟體層。如果您對這項技術感興趣，這堂課程可以滿足您的需求。有了 Google Kubernetes Engine，您就能在 Google Cloud 中以代管服務的形式使用 Kubernetes。
本課程的目標在於介紹 Google Kubernetes Engine (常簡稱為 GKE) 的基本概念，以及如何將應用程式容器化，以便在 Google Cloud 中執行。課程首先會初步介紹 Google Cloud，隨後簡介容器、Kubernetes、Kubernetes 架構和 Kubernetes 作業。
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這堂隨選密集課程會向參加人員說明 Google Cloud 提供的全方位彈性基礎架構和平台服務。這堂課結合了視訊講座、示範和實作研究室，可讓參加人員探索及部署解決方案元素，包括安全地建立互連網路、負載平衡、自動調度資源、基礎架構自動化，以及代管服務。
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This course helps learners create a study plan for the PCA (Professional Cloud Architect) certification exam. Learners explore the breadth and scope of the domains covered in the exam. Learners assess their exam readiness and create their individual study plan.
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In this course, you will be learning from ML Engineers and Trainers who work with the state-of-the-art development of ML pipelines here at Google Cloud. The first few modules will cover about TensorFlow Extended (or TFX), which is Google’s production machine learning platform based on TensorFlow for management of ML pipelines and metadata. You will learn about pipeline components and pipeline orchestration with TFX. You will also learn how you can automate your pipeline through continuous integration and continuous deployment, and how to manage ML metadata. Then we will change focus to discuss how we can automate and reuse ML pipelines across multiple ML frameworks such as tensorflow, pytorch, scikit learn, and xgboost. You will also learn how to use another tool on Google Cloud, Cloud Composer, to orchestrate your continuous training pipelines. And finally, we will go over how to use MLflow for managing the complete machine learning life cycle.
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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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In this course, you apply your knowledge of classification models and embeddings to build a ML pipeline that functions as a recommendation engine.
This is the fifth and final course of the Advanced Machine Learning on Google Cloud series.
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This course introduces the products and solutions to solve NLP problems on Google Cloud. Additionally, it explores the processes, techniques, and tools to develop an NLP project with neural networks by using Vertex AI and TensorFlow.
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This course describes different types of computer vision use cases and then highlights different machine learning strategies for solving these use cases. The strategies vary from experimenting with pre-built ML models through pre-built ML APIs and AutoML Vision to building custom image classifiers using linear models, deep neural network (DNN) models or convolutional neural network (CNN) models.
The course shows how to improve a model's accuracy with augmentation, feature extraction, and fine-tuning hyperparameters while trying to avoid overfitting the data.
The course also looks at practical issues that arise, for example, when one doesn't have enough data and how to incorporate the latest research findings into different models.
Learners will get hands-on practice building and optimizing their own image classification models on a variety of public datasets in the labs they will work on.
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This course covers how to implement the various flavors of production ML systems— static, dynamic, and continuous training; static and dynamic inference; and batch and online processing. You delve into TensorFlow abstraction levels, the various options for doing distributed training, and how to write distributed training models with custom estimators.
This is the second course of the Advanced Machine Learning on Google Cloud series. After completing this course, enroll in the Image Understanding with TensorFlow on Google Cloud course.
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This course takes a real-world approach to the ML Workflow through a case study. An ML team faces several ML business requirements and use cases. The team must understand the tools required for data management and governance and consider the best approach for data preprocessing.
The team is presented with three options to build ML models for two use cases. The course explains why they would use AutoML, BigQuery ML, or custom training to achieve their objectives.
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This course explores the benefits of using Vertex AI Feature Store, how to improve the accuracy of ML models, and how to find which data columns make the most useful features. This course also includes content and labs on feature engineering using BigQuery ML, Keras, and TensorFlow.
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This course covers building ML models with TensorFlow and Keras, improving the accuracy of ML models and writing ML models for scaled use.
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The course begins with a discussion about data: how to improve data quality and perform exploratory data analysis. We describe Vertex AI AutoML and how to build, train, and deploy an ML model without writing a single line of code. You will understand the benefits of Big Query ML. We then discuss how to optimize a machine learning (ML) model and how generalization and sampling can help assess the quality of ML models for custom training.
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This course explores what ML is and what problems it can solve. The course also discusses best practices for implementing machine learning. You’re introduced to Vertex AI, a unified platform to quickly build, train, and deploy AutoML machine learning models.
The course discusses the five phases of converting a candidate use case to be driven by machine learning, and why it’s important to not skip them. The course ends with recognizing the biases that ML can amplify and how to recognize them.
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This course introduces the Google Cloud big data and machine learning products and services that support the data-to-AI lifecycle. It explores the processes, challenges, and benefits of building a big data pipeline and machine learning models with Vertex AI on Google Cloud.
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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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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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Text Prompt Engineering Techniques introduces you to consider different strategic approaches & techniques to deploy when writing prompts for text-based generative AI tasks.
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This course will help ML Engineers, Developers, and Data Scientists implement Large Language Models for Generative AI use cases with Vertex AI. The first two modules of this course contain links to videos and prerequisite course materials that will build your knowledge foundation in Generative AI. Please do not skip these modules. The advanced modules in this course assume you have completed these earlier modules.
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本課程會介紹 Vertex AI Studio。您可以運用這項工具和生成式 AI 模型互動、根據商業構想設計原型，並投入到正式環境。透過身歷其境的應用實例、有趣的課程及實作實驗室，您將能探索從提示到正式環境的生命週期，同時學習如何將 Vertex AI Studio 運用在多模態版 Gemini 應用程式、提示設計、提示工程和模型調整。這個課程的目標是讓您能運用 Vertex AI Studio，在專案中發揮生成式 AI 的潛能。
This content is deprecated. Please see the latest version of the course, here.
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本課程將介紹擴散模型，這是一種機器學習模型，近期在圖像生成領域展現亮眼潛力。概念源自物理學，尤其深受熱力學影響。過去幾年來，在學術界和業界都是炙手可熱的焦點。在 Google Cloud 中，擴散模型是許多先進圖像生成模型和工具的基礎。課程將介紹擴散模型背後的理論，並說明如何在 Vertex AI 上訓練和部署這些模型。
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隨著企業持續擴大使用人工智慧和機器學習，以負責任的方式發展相關技術也日益重要。對許多企業來說，談論負責任的 AI 技術可能不難，如何付諸實行才是真正的挑戰。如要瞭解如何在機構中導入負責任的 AI 技術，本課程絕對能助您一臂之力。
您可以從中瞭解 Google Cloud 目前採取的策略、最佳做法和經驗談，協助貴機構奠定良好基礎，實踐負責任的 AI 技術。
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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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這個入門微學習課程主要介紹「負責任的 AI 技術」和其重要性，以及 Google 如何在自家產品中導入這項技術。本課程也會說明 Google 的 7 個 AI 開發原則。
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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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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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In "Architecting with Google Kubernetes Engine- Workloads", you'll embark on a comprehensive journey into cloud-native application development. Throughout the learning experience, you'll explore Kubernetes operations, deployment management, GKE networking, and persistent storage. This is the first course of the Architecting with Google Kubernetes Engine series. After completing this course, enroll in the Architecting with Google Kubernetes Engine- Production course.
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在 「Google Kubernetes Engine 架構：基礎知識」的課程中，您將復習 Google Cloud 的配置和原則，接著是建立和管理軟體容器簡介和 Kubernetes 架構簡介。
這是 Google Kubernetes Engine 架構系列中的第一項課程。完成此課程後，請註冊 Google Kubernetes Engine 架構：工作負載課程。
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這堂課程可讓參加人員瞭解如何使用確實有效的設計模式，在 Google Cloud 中打造相當可靠且效率卓越的解決方案。這堂課程接續了「設定 Google Compute Engine 架構」或「設定 Google Kubernetes Engine 架構」課程的內容，並假設參加人員曾實際運用上述任一課程涵蓋的技術。這堂課程結合了簡報、設計活動和實作研究室，可讓參加人員瞭解如何定義業務和技術需求，並在兩者之間取得平衡，設計出相當可靠、可用性高、安全又符合成本效益的 Google Cloud 部署項目。
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This course version is for non-English only. If you wish to take this course in English, please enroll here: Elastic Google Cloud Infrastructure: Scaling and Automation. If you wish to take it in another language, change your language in settings to see availability.
Migrate to Containers using the Migration Assessment Tool
Earned May 22, 2025 EDT
Oracle to BigQuery Migration
Earned May 21, 2025 EDT
Oracle to Cloud Spanner
Earned May 18, 2025 EDT
DEPRECATED Planning for a Google Workspace Deployment
Earned May 18, 2025 EDT
Inside Track: Looker Multistage Development Framework
Earned May 10, 2025 EDT
Modernize Infrastructure and Applications with Google Cloud
Earned May 10, 2025 EDT
High performance feature engineering for predictive and generative AI projects
Earned May 10, 2025 EDT
Innovating with Data and Google Cloud
Earned May 3, 2025 EDT
Introduction to Digital Transformation with Google Cloud
Earned May 3, 2025 EDT
Generative AI for Document Processing
Earned May 3, 2025 EDT
Modernization of Applications using Openshift on Google Cloud
Earned May 2, 2025 EDT
New Generative AI features in App Development
Earned May 2, 2025 EDT
Build Generative AI powered applications
Earned May 2, 2025 EDT
Data Warehousing for Partners: Enable Google Cloud Customers
Earned Apr 29, 2025 EDT
生成式 AI 適用的機器學習運作 (MLOps)
Earned Apr 27, 2025 EDT
On-Premises VMware to Compute Engine
Earned Apr 25, 2025 EDT
Partner Pre-Sales Readiness Training
Earned Apr 25, 2025 EDT
Partner Sales Readiness Training
Earned Apr 25, 2025 EDT
Upgrading your skills to work with Generative AI
Earned Apr 25, 2025 EDT
Enterprise Readiness in Generative AI
Earned Apr 23, 2025 EDT
Redshift to BigQuery
Earned Apr 19, 2025 EDT
Database Summit 2022
Earned Apr 18, 2025 EDT
Cloud Foundations: Customer Onboarding Best Practices
Earned Apr 16, 2025 EDT
Reinforcement Learning with Human Feedback (RLHF)
Earned Apr 14, 2025 EDT
Inside Track: Authoring and Connectors in Google Apigee Integration
Earned Apr 11, 2025 EDT
Inside Track: Cloud Network Operations: Network Monitoring and Troubleshooting
Earned Apr 10, 2025 EDT
Inside Track: Dataflow Advanced
Earned Apr 10, 2025 EDT
Introduction to Google Distributed Cloud air-gapped
Earned Apr 8, 2025 EDT
Trust and Security with Google Cloud
Earned Apr 4, 2025 EDT
Inside Track: SQL Server - Advanced
Earned Apr 3, 2025 EDT
Inside Track: Certificate Authority Service
Earned Apr 3, 2025 EDT
Analytics for SAP on Google Cloud
Earned Apr 3, 2025 EDT
SecOps on GDC for Tier 3 Analysts
Earned Mar 28, 2025 EDT
Technology + Beyond the UI
Earned Mar 27, 2025 EDT
Extends to Keep LookML DRY
Earned Mar 18, 2025 EDT
Table Calculations, Pivots, and Visualizations
Earned Mar 10, 2025 EDT
Elastic Cloud Infrastructure: Scaling and Automation
Earned Mar 2, 2025 EST
Introduction to SecOps on GDC
Earned Feb 15, 2025 EST
AI Services and GDC Deployments and Operations
Earned Feb 4, 2025 EST
Compute, Network, and Storage Services Configuration in GDC
Earned Feb 2, 2025 EST
L300 Google Distributed Cloud air-gapped
Earned Jan 27, 2025 EST
GDC Platform Introduction
Earned Jan 3, 2025 EST
L200 Google Distributed Cloud air-gapped
Earned Dec 19, 2024 EST
L200 Google Distributed Cloud Connected
Earned Dec 14, 2024 EST
Innovating with Google Cloud Artificial Intelligence
Earned Dec 12, 2024 EST
Generative AI in App Integration
Earned Dec 12, 2024 EST
Gemini in Google Slides
Earned Dec 11, 2024 EST
Building AI with Colab Enterprise
Earned Dec 10, 2024 EST
Inside Track: DORA
Earned Dec 10, 2024 EST
Accelerating Generative AI pipelines with GPUs and TPUs
Earned Dec 9, 2024 EST
Experimenting and Evaluating your Gen AI models
Earned Dec 8, 2024 EST
Operationalizing large scale machine learning (ML) on Cloud TPUs with Google Kubernetes Engine (GKE)
Earned Dec 8, 2024 EST
Google Cloud: Prompt Engineering Guide
Earned Dec 7, 2024 EST
Build a Certification Study Guide: PMLE
Earned Dec 6, 2024 EST
Introduction to Security in the World of AI
Earned Dec 6, 2024 EST
Introduction to Reliable Deep Learning
Earned Dec 5, 2024 EST
Machine Learning Operations (MLOps) with Vertex AI: Manage Features
Earned Nov 23, 2024 EST
Serverless Data Processing with Dataflow: Operations
Earned Nov 15, 2024 EST
Networking in Google Cloud: Network Architecture
Earned Nov 12, 2024 EST
Networking in Google Cloud: Routing and Addressing
Earned Nov 11, 2024 EST
Google Cloud 運算的基本概念：Google Cloud 中的資料、機器學習和 AI
Earned Oct 28, 2024 EDT
Google Cloud 運算的基本概念：Google Cloud 基礎架構
Earned Oct 22, 2024 EDT
Google Cloud 資料分析簡介
Earned Oct 7, 2024 EDT
Enterprise Search on Generative AI App Builder
Earned Sep 28, 2024 EDT
Google Security Operations - SIEM Rules
Earned Sep 3, 2024 EDT
Google Security Operations - SOAR Analyst
Earned Aug 26, 2024 EDT
Security Practices with Google Security Operations - SIEM
Earned Aug 13, 2024 EDT
Google Security Operations - SOAR Developer
Earned Aug 12, 2024 EDT
Unlocking the Power of Google Cloud Generative AI for Partners
Earned Aug 3, 2024 EDT
Selling the Platform & Building Client Trust
Earned Aug 3, 2024 EDT
Google Cloud Generative AI Trailblazer
Earned Aug 3, 2024 EDT
Google Security Operations - Deep Dive
Earned Jul 27, 2024 EDT
Google Security Operations - Fundamentals
Earned Jul 25, 2024 EDT
[DEPRECATED] SOAR Fundamentals
Earned Jul 17, 2024 EDT
Mandiant Fundamentals
Earned Jul 17, 2024 EDT
Chronicle SIEM Fundamentals
Earned Jul 17, 2024 EDT
Google Cloud 運算的基本概念：Cloud 運算基礎知識
Earned Jul 12, 2024 EDT
Google API products: A key to modern application development
Earned Jul 3, 2024 EDT
機器學習簡介：語言處理
Earned Jul 3, 2024 EDT
VM Migration for Partners
Earned Jul 2, 2024 EDT
Managing Change when Moving to Google Cloud
Earned Jul 2, 2024 EDT
Rapid Migration & Modernization Program
Earned Jun 26, 2024 EDT
Managing Security in Google Cloud
Earned Jun 20, 2024 EDT
Preparing for Your Professional Cloud Security Engineer Journey
Earned Jun 11, 2024 EDT
Hybrid Cloud Infrastructure Foundations with Anthos
Earned Jun 5, 2024 EDT
Custom Search with Embeddings in Vertex AI
Earned May 28, 2024 EDT
Introduction to Gemini Enterprise for Customer Experience and Conversational Agents
Earned May 21, 2024 EDT
Developing a Google SRE Culture
Earned May 20, 2024 EDT
使用 Gemini：端對端 SDLC
Earned May 12, 2024 EDT
Improving developer velocity with Gemini Code Assist
Earned May 10, 2024 EDT
使用 Gemini：數據資料學家和分析師
Earned May 3, 2024 EDT
使用 Gemini：資安工程師
Earned May 2, 2024 EDT
使用 Gemini：DevOps 工程師
Earned Apr 20, 2024 EDT
使用 Gemini：網路工程師
Earned Apr 19, 2024 EDT
使用 Gemini：應用程式開發人員
Earned Apr 18, 2024 EDT
使用 Gemini：雲端架構師
Earned Apr 15, 2024 EDT
Building Gen AI Apps with Vertex AI: Prompting and Tuning
Earned Apr 13, 2024 EDT
Modernizing Mainframe Applications with Google Cloud
Earned Apr 8, 2024 EDT
Search with AI Applications
Earned Mar 25, 2024 EDT
Vector Search 和嵌入
Earned Mar 18, 2024 EDT
Serverless Data Processing with Dataflow: Foundations
Earned Feb 24, 2024 EST
Building Resilient Streaming Systems on Google Cloud Platform
Earned Feb 17, 2024 EST
Smart Analytics, Machine Learning, and AI on Google Cloud
Earned Feb 17, 2024 EST
Build Batch Data Pipelines on Google Cloud
Earned Jan 31, 2024 EST
Build Data Lakes and Data Warehouses on Google Cloud
Earned Jan 22, 2024 EST
Preparing for your Professional Data Engineer Journey
Earned Jan 11, 2024 EST
Conversational AI on Vertex AI and Dialogflow CX
Earned Dec 9, 2023 EST
開始使用 Google Kubernetes Engine
Earned Nov 26, 2023 EST
彈性的 Google Cloud 基礎架構：資源調度與自動化
Earned Nov 24, 2023 EST
Preparing for your Professional Cloud Architect Journey
Earned Nov 14, 2023 EST
ML Pipelines on Google Cloud
Earned Oct 24, 2023 EDT
Machine Learning Operations (MLOps): Getting Started
Earned Oct 18, 2023 EDT
Recommendation Systems on Google Cloud
Earned Oct 17, 2023 EDT
Natural Language Processing on Google Cloud
Earned Oct 11, 2023 EDT
Computer Vision Fundamentals with Google Cloud
Earned Oct 7, 2023 EDT
Production Machine Learning Systems
Earned Oct 1, 2023 EDT
Machine Learning in the Enterprise
Earned Sep 28, 2023 EDT
Feature Engineering
Earned Sep 19, 2023 EDT
Build, Train and Deploy ML Models with Keras on Google Cloud
Earned Sep 16, 2023 EDT
Launching into Machine Learning
Earned Sep 14, 2023 EDT
How Google Does Machine Learning
Earned Sep 8, 2023 EDT
Google Cloud Big Data and Machine Learning Fundamentals
Earned Sep 5, 2023 EDT
Google Cloud 的 AI 和機器學習服務簡介
Earned Sep 1, 2023 EDT
Generative AI for Business Leaders
Earned Aug 28, 2023 EDT
Text Prompt Engineering Techniques
Earned Aug 26, 2023 EDT
Implementing Generative AI with Vertex AI
Earned Aug 24, 2023 EDT
Vertex AI Studio 簡介
Earned Aug 23, 2023 EDT
建立圖像說明生成模型
Earned Aug 22, 2023 EDT
Transformer 和 BERT 模型
Earned Aug 22, 2023 EDT
編碼器-解碼器架構
Earned Aug 22, 2023 EDT
注意力機制
Earned Aug 21, 2023 EDT
Generative AI Explorer : Vertex AI
Earned Jul 30, 2023 EDT
圖像生成簡介
Earned Jul 29, 2023 EDT
負責任的 AI 技術：透過 Google Cloud 採用 AI 開發原則
Earned Jul 29, 2023 EDT
Generative AI Fundamentals
Earned Jul 28, 2023 EDT
負責任的 AI 技術簡介
Earned Jul 28, 2023 EDT
大型語言模型簡介
Earned Jul 28, 2023 EDT
生成式 AI 簡介
Earned Jul 27, 2023 EDT
Architecting with Google Kubernetes Engine: Workloads
Earned Oct 14, 2021 EDT
Architecting with Google Kubernetes Engine: Foundations - 繁體中文
Earned Oct 5, 2021 EDT
可靠的 Google Cloud 基礎架構：設計與程序
Earned Sep 22, 2021 EDT
Elastic Google Cloud Infrastructure: Scaling and Automation - Locales
Earned Sep 20, 2021 EDT