Serverless Data Processing with Dataflow: Operations
Earned Jan 3, 2025 EST
[DEPRECATED] Advanced Performance Measurement
Earned Nov 12, 2024 EST
Conversational AI Voice and Chat Integrations
Earned Nov 12, 2024 EST
[Deprecated] Advanced Webhook Concepts
Earned Nov 12, 2024 EST
Advanced Conversation Design
Earned Nov 12, 2024 EST
Conversational AI and its Engagement Framework
Earned Nov 7, 2024 EST
DEPRECATED Planning for a Google Workspace Deployment
Earned Nov 4, 2024 EST
Google Workspace Data Governance
Earned Nov 4, 2024 EST
Google Workspace Security
Earned Oct 24, 2024 EDT
Google Workspace Core Services
Earned Oct 24, 2024 EDT
Google Workspace User and Resource Management
Earned Oct 24, 2024 EDT
Generative AI Fundamentals
Earned Jul 5, 2024 EDT
在 Vertex AI 上构建和部署机器学习解决方案
Earned Jun 28, 2024 EDT
Serverless Data Processing with Dataflow: Foundations
Earned Jun 27, 2024 EDT
Building Resilient Streaming Systems on Google Cloud Platform
Earned Jun 21, 2024 EDT
Build Data Lakes and Data Warehouses on Google Cloud
Earned Jun 18, 2024 EDT
Preparing for your Professional Data Engineer Journey
Earned Jun 14, 2024 EDT
Machine Learning Operations (MLOps): Getting Started
Earned Jun 13, 2024 EDT
在 Google Cloud 上为机器学习 API 准备数据
Earned Jun 13, 2024 EDT
Machine Learning Operations (MLOps) with Vertex AI: Manage Features
Earned Jun 10, 2024 EDT
Recommendation Systems on Google Cloud
Earned Jun 7, 2024 EDT
Natural Language Processing on Google Cloud
Earned Jun 5, 2024 EDT
Production Machine Learning Systems
Earned May 30, 2024 EDT
Machine Learning in the Enterprise
Earned May 29, 2024 EDT
Feature Engineering
Earned May 28, 2024 EDT
Build, Train and Deploy ML Models with Keras on Google Cloud
Earned May 27, 2024 EDT
Integrate Vertex AI Search and Conversation into Voice and Chat Apps
Earned May 23, 2024 EDT
Develop Advanced Enterprise Search and Conversation Applications
Earned May 21, 2024 EDT
Custom Search with Embeddings in Vertex AI
Earned May 21, 2024 EDT
图像生成简介
Earned May 16, 2024 EDT
Generative AI Fundamentals - 简体中文
Earned May 16, 2024 EDT
Generative AI Explorer : Vertex AI
Earned May 15, 2024 EDT
矢量搜索和嵌入
Earned May 15, 2024 EDT
Implementing Generative AI with Vertex AI
Earned May 15, 2024 EDT
Text Prompt Engineering Techniques
Earned May 14, 2024 EDT
Vertex AI Studio 简介
Earned May 14, 2024 EDT
Generative AI for Business Leaders
Earned May 13, 2024 EDT
Responsible AI: 和 Google Cloud 一起践行 AI 原则
Earned May 10, 2024 EDT
负责任的 AI 简介
Earned May 10, 2024 EDT
大型语言模型简介
Earned May 10, 2024 EDT
生成式 AI 简介
Earned May 10, 2024 EDT
创建图片标注模型
Earned May 10, 2024 EDT
Transformer 模型和 BERT 模型
Earned May 9, 2024 EDT
编码器-解码器架构
Earned May 9, 2024 EDT
注意力机制
Earned May 9, 2024 EDT
Launching into Machine Learning
Earned May 2, 2024 EDT
Google Cloud 上的 AI 和机器学习简介
Earned Apr 29, 2024 EDT
Complete the Extend Gemini with controlled generation and Tool use skill badge to demonstrate your proficiency in connecting models to external tools and APIs. This allows models to augment their knowledge, extend their capabilities and interact with external systems to take actions such as sending an email.
A skill badge is an exclusive digital badge issued by Google Cloud in recognition of your proficiency with Google Cloud products and services and tests your ability to apply your knowledge in an interactive hands-on environment. Complete the assessment challenge lab, to receive a skill badge that you can share with your network.
When you complete this course, you can earn the badge displayed here and claim it on Credly! Boost your cloud career by showing the world the skills you have developed!"
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An LLM-based application can process language in a way that resembles thought. But if you want to extend its capabilities to take actions by running other functions you have coded, you will need to use function calling. This can also be referred to as tool use. Additionally, you can give a model the ability to search Google or search a data store of documents to ground its responses. In other words, to base its answers on that information. In this course, you’ll explore these concepts.
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Learn a variety of strategies and techniques to engineer effective prompts for generative models
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Learn how to leverage Gemini multimodal capabilities to process and generate text, images, and audio and to integrate Gemini through APIs to perform tasks such as content creation and summarization.
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The course aims to train Google technical sales partners on the business value discovery process using proprietary content.
Course activities use an external tool (Yoodli). Refer to Yoodli's Terms of Service and Privacy Notice.
Note: The Yoodli Labs in this course will be deprecated on March 31st. We encourage you to finish your remaining Yoodli Labs before the March 31 deadline.
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“生成式 AI 智能体：助力组织转型”是“Gen AI Leader”学习路线中的第五门课程，也是最后一门课程。本课程探讨了组织如何使用量身定制的生成式 AI 智能体，帮助应对特定的业务挑战。您将亲自动手构建一个基本的生成式 AI 智能体，并探索这些智能体的组成部分，例如模型、推理循环以及各种工具。
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“生成式 AI 应用：改变工作方式”是 Generative AI Leader 学习路线的第四门课程。本课程介绍 Google 的生成式 AI 应用，例如 Gemini for Workspace 和 NotebookLM。它将引导您逐一了解接地、检索增强生成、构建有效提示和构建自动化工作流等概念。
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“生成式 AI: 全面了解生成式 AI”是 Generative AI Leader 学习路线中的第三门课程。生成式 AI 正在改变我们的工作方式,以及我们与周围世界的互动方式。作为领导者,应该如何利用生成式 AI 来推动实现实际的业务成果?在本课程中,您将探索构建生成式 AI 解决方案的不同层级、Google Cloud 的产品,以及选择解决方案时需要考虑的因素。
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“生成式 AI: 剖析基本概念”是 Generative AI Leader 学习路线中的第二门课程。在本课程中,您将了解生成式 AI 的基本概念。您要探索 AI、机器学习和生成式 AI 之间的区别,了解各种数据类型如何赋能生成式 AI,从而应对各种业务挑战。您还将深入了解 Google Cloud 应对基础模型局限性的策略,以及负责任和安全的 AI 开发与部署面临着哪些关键挑战。
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“生成式 AI：不只是聊天机器人”是 Generative AI Leader 学习路线中的第一门课程。学习本课程没有知识门槛。本课程旨在帮助您超越对聊天机器人的基本认知,探索生成式 AI技术为您的组织带来的真正潜力。您将探索基础模型和提示工程等概念,这些知识对利用生成式 AI 的强大功能至关重要。本课程还将说明,为组织制定成功的生成式 AI 策略时,需要考虑哪些重要因素。
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此课程将探索如何使用 AI 功能套件 Gemini in BigQuery 为“数据到 AI”工作流提供助力。其中涉及到的功能包括数据探索和准备、代码生成和问题排查，以及工作流发现和可视化。此课程包含概念解释、真实使用场景以及实操实验等内容，可帮助数据从业者提升效率并加快流水线开发速度。
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在本课程中，您将了解 Google Cloud 数据工程、数据工程师的角色和职责，以及相关的 Google Cloud 产品和服务。您还将了解如何应对数据工程挑战。
Complete the Extend Gemini Enterprise Assistant Capabilities skill badge to demonstrate your ability to extend Gemini Enterprise assistant's capabilities with actions, grounding with Google Search, and a conversational agent.
A skill badge is an exclusive digital badge issued by Google Cloud in recognition of your proficiency with Google Cloud products and services and tests your ability to apply your knowledge in an interactive hands-on environment. Complete the assessment challenge lab, to receive a skill badge that you can share with your network.
When you complete this course, you can earn the badge displayed here and claim it on Credly! Boost your cloud career by showing the world the skills you have developed!
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Gemini Enterprise 结合了 Google 在搜索和 AI 领域的专长。它是一款强大的工具，让员工只需通过一个搜索栏，就能从文档库、邮件、聊天消息、工单系统及其他数据源中查找具体信息。Gemini Enterprise 助理还能帮助进行头脑风暴、开展研究、生成文档大纲并执行其他操作，比如邀请同事参加某个日历活动。因此它能加快知识型工作的进度并提升协作效率。（请注意，Gemini Enterprise 以前称为 Google Agentspace，本课程中可能会提及以前的产品名称。）
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In this skill badge, you will demonstrate your ability to deploy Google Agentspace and set up data stores and actions. To learn these skills, we encourage you to take the course Accelerate Knowledge Exchange with Agentspace.
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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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In this course, you will learn about advanced methods and tools to monitor the performance of your virtual agents in Dialogflow CX
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Learn about building conversational AI voice and chat integrations, including how telephony systems can connect with Google to enable phone-based interactions within the Conversational AI ecosystem. Explore key topics such as the differences between chat and voice conversations, the writing process for creating conversation scripts, and the beginning of the interrogative series and closing sequence.
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This course explores advanced technical considerations to optimize Webhook connectivity for comprehensive, end-to-end, Virtual Agent self-service experiences.
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In this course, you will learn the advanced conversational design principles for both the Voice and Caht channels to craft engaging and effective end-to-end experiences that emulate human-like interactions.
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Transform your understanding of customer service with this course on the Customer Engagement Suite (CES) and its powerful generative AI capabilities. You'll start by tracing the journey of contact centers, understanding how they've evolved and where gen AI is propelling them next. Then, you'll gain a deep understanding of the core building blocks within the CES solution, seeing how each component contributes to delivering exceptional customer experiences. The course concludes by exploring the robust business case for CES, along with practical use cases and the various user personas that benefit from this innovative solution.
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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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This course equips learners with skills to govern data within their Google Workspace environment. Learners will explore data loss prevention rules in Gmail and Drive to prevent data leakage. They will then learn how to use Google Vault for data retention, preservation, and retrieval purposes. Next, they will learn how to configure data regions and export settings to align with regulations. Finally, learners will discover how to classify data using labels for enhanced organization and security.
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This course empowers learners to secure their Google Workspace environment. Learners will implement strong password policies and two-step verification to govern user access. They will then utilize the security investigation tool to proactively identify and respond to security risks. Next, they will manage third-party app access and mobile devices to ensure security. Finally, learners will enforce email security and compliance measures to protect organizational data.
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This course was designed to give learners a comprehensive understanding of Google Workspace core services. Learners will explore enabling, disabling, and configuring settings for these services, including Gmail, Calendar, Drive, Meet, Chat, and Docs. Next, they'll learn how to deploy and manage Gemini to empower their users. Finally, learners will examine use cases for AppSheet and Apps Script to automate tasks and extend the functionality of Google Workspace applications.
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This course was designed to provide an understanding of user and resource management in Google Workspace. Learners will explore the configuration of organizational units to align with their organization's needs. Additionally, learners will discover how to manage various types of Google Groups. They will also develop expertise in managing domain settings within Google Workspace. Finally, learners will master the optimization and structuring of resources within their Google Workspace environment.
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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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完成在 Vertex AI 上构建和部署机器学习解决方案课程，赢取中级技能徽章。 在此课程中，您将了解如何使用 Google Cloud 的 Vertex AI Platform、AutoML 以及自定义训练服务来 训练、评估、调优、解释和部署机器学习模型。 此技能徽章课程的目标受众是专业的数据科学家和机器学习 工程师。 技能徽章是由 Google Cloud 颁发的专属数字徽章，旨在认可 您对 Google Cloud 产品与服务的熟练度；您需要在 交互式实操环境中参加考核，证明自己运用所学知识的能力后才能获得此徽章。完成此技能徽章课程 和作为最终评估的实验室挑战赛，即可获得数字徽章， 在您的人际圈中炫出自己的技能。
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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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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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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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完成入门级技能徽章课程在 Google Cloud 上为机器学习 API 准备数据，展示以下技能： 使用 Dataprep by Trifacta 清理数据、在 Dataflow 中运行数据流水线、在 Dataproc 中创建集群和运行 Apache Spark 作业，以及调用机器学习 API，包括 Cloud Natural Language API、Google Cloud Speech-to-Text API 和 Video Intelligence API。
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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 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 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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This course on Integrate Vertex AI Search and Conversation into Voice and Chat Apps is composed of a set of labs to give you a hands on experience to interacting with new Generative AI technologies. You will learn how to create end-to-end search and conversational experiences by following examples. These technologies complement predefined intent-based chat experiences created in Dialogflow with LLM-based, generative answers that can be based on your own data. Also, they allow you to porvide enterprise-grade search experiences for internal and external websites to search documents, structure data and public websites.
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In this course, you'll use text embeddings for tasks like classification, outlier detection, text clustering and semantic search. You'll combine semantic search with the text generation capabilities of an LLM to build Retrieval Augmented Generation (RAG) solutions, such as for question-answering systems, using Google Cloud's Vertex AI and Google Cloud databases.
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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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本课程向您介绍扩散模型。这类机器学习模型最近在图像生成领域展现出了巨大潜力。扩散模型的灵感来源于物理学，特别是热力学。过去几年内，扩散模型成为热门研究主题并在整个行业开始流行。Google Cloud 上许多先进的图像生成模型和工具都是以扩散模型为基础构建的。本课程向您介绍扩散模型背后的理论，以及如何在 Vertex AI 上训练和部署此类模型。
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完成 Introduction to Generative AI、Introduction to Large Language Models 和 Introduction to Responsible AI 三门课程，赢取技能徽章。通过最终测验，即表明您理解了生成式 AI 的基本概念。
技能徽章是由 Google Cloud 颁发的数字徽章，旨在认可您对 Google Cloud 产品与服务的了解程度。公开您的个人资料并将技能徽章添加到您的社交媒体个人资料中，以此来分享您获得的成就。
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This content is deprecated. Please see the latest version of the course, here.
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在本次课程中，探索 AI 赋能的搜索技术、工具和应用。学习利用向量嵌入的语义搜索、融合语义和关键字的混合搜索方法，以及检索增强生成 (RAG) 技术，以打造基于事实的 AI 智能体，尽可能减少 AI 幻觉。获取 Vertex AI Vector Search 实战经验，打造您自己的智能搜索引擎。
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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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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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本课程介绍 Vertex AI Studio，这是一种用于与生成式 AI 模型交互、围绕业务创意进行原型设计并在生产环境中落地的工具。通过沉浸式应用场景、富有吸引力的课程和实操实验，您将探索从提示到产品的整个生命周期，了解如何将 Vertex AI Studio 用于多模态 Gemini 应用、提示设计、提示工程和模型调优。本课程的目的在于帮助您利用 Vertex AI Studio，在自己的项目中充分发掘生成式 AI 的潜力。
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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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随着企业对人工智能和机器学习的应用越来越广泛，以负责任的方式构建这些技术也变得更加重要。但对很多企业而言，真正践行 Responsible AI 并非易事。如果您有意了解如何在组织内践行 Responsible AI，本课程正适合您。
本课程将介绍 Google Cloud 目前如何践行 Responsible AI，以及从中总结的最佳实践和经验教训，便于您以此为框架构建自己的 Responsible AI 方法。
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这是一节入门级微课程，旨在解释什么是负责任的 AI、它的重要性，以及 Google 如何在自己的产品中实现负责任的 AI。此外，本课程还介绍了 Google 的 7 个 AI 开发原则。
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这是一节入门级微学习课程，探讨什么是大型语言模型 (LLM)、适合的应用场景以及如何使用提示调整来提升 LLM 性能，还介绍了可以帮助您开发自己的 Gen AI 应用的各种 Google 工具。
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这是一节入门级微课程，旨在解释什么是生成式 AI、它的用途以及与传统机器学习方法的区别。该课程还介绍了可以帮助您开发自己的生成式 AI 应用的各种 Google 工具。
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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本课程介绍 Google Cloud 的 AI 和机器学习 (ML) 能力，重点讲解如何开发生成式和预测式 AI 项目。本课程将探讨“数据到 AI”全生命周期中的多种技术、产品和工具，并通过互动练习帮助数据科学家、AI 开发者和机器学习工程师提升专业能力。
Extend Gemini with controlled generation and Tool use
Earned Mar 27, 2026 EDT
Empower Gen AI apps with tool use
Earned Mar 26, 2026 EDT
Engineer Effective Prompts for Generative Models
Earned Mar 23, 2026 EDT
Explore Google's Gen AI Models
Earned Mar 23, 2026 EDT
Discover Business Value for Customers
Earned Mar 19, 2026 EDT
生成式 AI 智能体：助力组织转型
Earned Feb 18, 2026 EST
生成式 AI 应用：改变工作方式
Earned Feb 18, 2026 EST
生成式 AI: 全面了解生成式 AI
Earned Feb 18, 2026 EST
生成式 AI：剖析基本概念
Earned Feb 18, 2026 EST
生成式 AI：不只是聊天机器人
Earned Feb 18, 2026 EST
使用 Gemini in BigQuery 提高效率
Earned Jan 9, 2026 EST
Google Cloud 数据工程简介
Earned Jan 8, 2026 EST
使用多模态 Gemini 和多模态 RAG 检查富文档
Earned Jul 29, 2025 EDT
Extend Gemini Enterprise Assistant Capabilities
Earned Jul 29, 2025 EDT
借助 Gemini Enterprise 加速知识交流
Earned Jul 29, 2025 EDT
Deploy Google Agentspace
Earned Jul 29, 2025 EDT
Serverless Data Processing with Dataflow: Operations
Earned Jan 3, 2025 EST
[DEPRECATED] Advanced Performance Measurement
Earned Nov 12, 2024 EST
Conversational AI Voice and Chat Integrations
Earned Nov 12, 2024 EST
[Deprecated] Advanced Webhook Concepts
Earned Nov 12, 2024 EST
Advanced Conversation Design
Earned Nov 12, 2024 EST
Conversational AI and its Engagement Framework
Earned Nov 7, 2024 EST
DEPRECATED Planning for a Google Workspace Deployment
Earned Nov 4, 2024 EST
Google Workspace Data Governance
Earned Nov 4, 2024 EST
Google Workspace Security
Earned Oct 24, 2024 EDT
Google Workspace Core Services
Earned Oct 24, 2024 EDT
Google Workspace User and Resource Management
Earned Oct 24, 2024 EDT
Generative AI Fundamentals
Earned Jul 5, 2024 EDT
在 Vertex AI 上构建和部署机器学习解决方案
Earned Jun 28, 2024 EDT
Serverless Data Processing with Dataflow: Foundations
Earned Jun 27, 2024 EDT
Building Resilient Streaming Systems on Google Cloud Platform
Earned Jun 21, 2024 EDT
Build Data Lakes and Data Warehouses on Google Cloud
Earned Jun 18, 2024 EDT
Preparing for your Professional Data Engineer Journey
Earned Jun 14, 2024 EDT
Machine Learning Operations (MLOps): Getting Started
Earned Jun 13, 2024 EDT
在 Google Cloud 上为机器学习 API 准备数据
Earned Jun 13, 2024 EDT
Machine Learning Operations (MLOps) with Vertex AI: Manage Features
Earned Jun 10, 2024 EDT
Recommendation Systems on Google Cloud
Earned Jun 7, 2024 EDT
Natural Language Processing on Google Cloud
Earned Jun 5, 2024 EDT
Production Machine Learning Systems
Earned May 30, 2024 EDT
Machine Learning in the Enterprise
Earned May 29, 2024 EDT
Feature Engineering
Earned May 28, 2024 EDT
Build, Train and Deploy ML Models with Keras on Google Cloud
Earned May 27, 2024 EDT
Integrate Vertex AI Search and Conversation into Voice and Chat Apps
Earned May 23, 2024 EDT
Develop Advanced Enterprise Search and Conversation Applications
Earned May 21, 2024 EDT
Custom Search with Embeddings in Vertex AI
Earned May 21, 2024 EDT
图像生成简介
Earned May 16, 2024 EDT
Generative AI Fundamentals - 简体中文
Earned May 16, 2024 EDT
Generative AI Explorer : Vertex AI
Earned May 15, 2024 EDT
矢量搜索和嵌入
Earned May 15, 2024 EDT
Implementing Generative AI with Vertex AI
Earned May 15, 2024 EDT
Text Prompt Engineering Techniques
Earned May 14, 2024 EDT
Vertex AI Studio 简介
Earned May 14, 2024 EDT
Generative AI for Business Leaders
Earned May 13, 2024 EDT
Responsible AI: 和 Google Cloud 一起践行 AI 原则
Earned May 10, 2024 EDT
负责任的 AI 简介
Earned May 10, 2024 EDT
大型语言模型简介
Earned May 10, 2024 EDT
生成式 AI 简介
Earned May 10, 2024 EDT
创建图片标注模型
Earned May 10, 2024 EDT
Transformer 模型和 BERT 模型
Earned May 9, 2024 EDT
编码器-解码器架构
Earned May 9, 2024 EDT
注意力机制
Earned May 9, 2024 EDT
Launching into Machine Learning
Earned May 2, 2024 EDT
Google Cloud 上的 AI 和机器学习简介
Earned Apr 29, 2024 EDT