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Omid Monfared

成为会员时间：2022

Build a Certification Study Guide: PCA Exam Prep Earned Jul 25, 2024 EDT
为 Compute Engine 实现云负载均衡 Earned Nov 27, 2023 EST
Architecting with Google Kubernetes Engine: Production Earned Oct 25, 2023 EDT
Architecting with Google Kubernetes Engine: Workloads Earned Oct 10, 2023 EDT
Architecting with Google Kubernetes Engine: Foundations - 简体中文 Earned Oct 4, 2023 EDT
Google Cloud 重要基础设施：核心服务 Earned Sep 30, 2023 EDT
Google Cloud 重要基础设施：基础 Earned Sep 29, 2023 EDT
Google Kubernetes Engine 使用入门 Earned Sep 28, 2023 EDT
可靠的 Google Cloud 基础设施: 设计和流程 Earned Sep 23, 2023 EDT
Computer Vision Fundamentals with Google Cloud Earned Sep 21, 2023 EDT
Planetary Scale Earth Observation with Google Earth Engine Earned Sep 12, 2023 EDT
Security Practices with Google Security Operations - SIEM Earned Sep 12, 2023 EDT
Natural Language Processing on Google Cloud Earned Sep 12, 2023 EDT
Machine Learning Operations (MLOps): Getting Started Earned Sep 8, 2023 EDT
Launching into Machine Learning Earned Sep 7, 2023 EDT
Build Streaming Data Pipelines on Google Cloud Earned Sep 6, 2023 EDT
[DEPRECATED] Google Cloud Data Solutions for the Public Sector Earned Sep 5, 2023 EDT
DEPRECATED Create Conversational AI Agents with Dialogflow CX Earned Sep 4, 2023 EDT
Responsible AI: 和 Google Cloud 一起践行 AI 原则 Earned Sep 3, 2023 EDT
Vertex AI Studio 简介 Earned Sep 3, 2023 EDT
探索生成式 AI - Agent Platform Earned Sep 3, 2023 EDT
Google Cloud AI and ML Solutions for the Public Sector Earned Sep 2, 2023 EDT
创建图片标注模型 Earned Jun 21, 2023 EDT
Transformer 模型和 BERT 模型 Earned Jun 20, 2023 EDT
编码器-解码器架构 Earned Jun 19, 2023 EDT
注意力机制 Earned Jun 19, 2023 EDT
图像生成简介 Earned Jun 16, 2023 EDT
Generative AI Fundamentals - 简体中文 Earned Jun 16, 2023 EDT
负责任的 AI 简介 Earned Jun 15, 2023 EDT
大型语言模型简介 Earned Jun 15, 2023 EDT
生成式 AI 简介 Earned Jun 5, 2023 EDT
[Depricated] Build, Train and Deploy ML Models with Keras on Google Cloud Earned Jan 17, 2023 EST
How Google Does Machine Learning Earned Jan 9, 2023 EST
[DEPRECATED] Google Cloud Big Data and Machine Learning Fundamentals Earned Dec 29, 2022 EST
Google Cloud 基础知识：核心基础设施 Earned Dec 28, 2022 EST

Learn how to use Gemini Notebook to create a personalized study guide for the Professional Cloud Architect certification exam. You'll review Gemini Notebook features, create a notebook in Gemini Notebook, and learn how to use a study guide to practice for a certification exam.

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完成入门级技能徽章课程为 Compute Engine 实现云负载均衡，展示以下方面的技能： 在 Compute Engine 中创建和部署虚拟机 以及配置网络和应用负载均衡器。

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In this course, you'll learn about Kubernetes and Google Kubernetes Engine (GKE) security; logging and monitoring; and using Google Cloud managed storage and database services from within GKE. This is the second course of the Architecting with Google Kubernetes Engine series. After completing this course, enroll in the Reliable Google Cloud Infrastructure: Design and Process course or the Hybrid Cloud Infrastructure Foundations with Anthos course.

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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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在本课程“Google Kubernetes Engine 架构设计：基础知识”中，您将了解 Google Cloud 的概况和原理，然后学习如何创建和管理软件容器，以及了解 Kubernetes 的架构。 这是“Google Kubernetes Engine 架构设计”系列课程的第一门课程。完成本课程后，请报名参加“Google Kubernetes Engine 架构设计：工作负载”课程。

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这门自助式速成课程向学员介绍 Google Cloud 提供的灵活全面的基础架构和平台服务，着重介绍了 Compute Engine。学员将通过一系列视频讲座、演示和动手实验，探索和部署各种解决方案元素，包括网络、系统和应用服务等基础架构组件。本课程的内容还包括如何部署实用的解决方案，包括客户提供的加密密钥、安全和访问权限管理、配额和结算，以及资源监控。

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这门自助式速成课程向学员介绍 Google Cloud 提供的灵活全面的基础架构和平台服务，其中着重介绍了 Compute Engine。学员将通过一系列视频讲座、演示和动手实验，探索和部署各种解决方案元素，包括网络、虚拟机和应用服务等基础架构组件。您将学习如何通过控制台和 Cloud Shell 使用 Google Cloud。您还将了解云架构师角色、基础架构设计方法以及虚拟网络配置和虚拟私有云 (VPC)、项目、网络、子网、IP 地址、路由及防火墙规则。

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欢迎学习“Google Kubernetes Engine 使用入门”课程。Kubernetes 是位于应用和硬件基础架构之间的软件层，如果您对 Kubernetes 感兴趣，那就来对地方了！Google Kubernetes Engine 将 Kubernetes 作为 Google Cloud 上的代管式服务提供给您使用。 本课程的目标是介绍 Google Kubernetes Engine（通常称为 GKE）的基础知识，以及将应用容器化并在 Google Cloud 中运行的方法。本课程首先介绍 Google Cloud 的基础知识，然后概述容器、Kubernetes、Kubernetes 架构以及 Kubernetes 操作。

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本课程指导学员运用久经考验的设计模式在 Google Cloud 上构建高度可靠且高效的解决方案。它是“Google Compute Engine 架构设计”或“Google Kubernetes Engine 架构设计”课程的延续，并假定您有使用其中任何一门课程所涵盖技术的实践经验。通过一系列演示、设计活动和动手实验，学员可以了解如何定义及平衡业务要求和技术要求，以便设计可靠性和可用性高、安全且经济实惠的 Google Cloud 部署。

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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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Learn how public sector agencies and academic researchers leverage Google Earth Engine to distill meaningful insights from petabytes of Earth Observation at planetary scale. Google Earth Engine is a fully managed geospatial platform that has enabled research for more than a decade in environmental areas such as forestry, agriculture, water, and sustainability.

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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 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 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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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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In this course you will get hands-on in order to work through real-world challenges faced when building streaming data pipelines. The primary focus is on managing continuous, unbounded data with Google Cloud products.

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Enterprise data sharing made easy with Dataplex and Analytics Hub Learn how to share data securely in your lakehouse with minimized data duplication and more data governance through Dataplex and Analytics Hub - enterprise data management made easy. Creating Data Pipelines with Data Fusion In this session, we will explore using Data Fusion to create code-free point and click pipelines that can ETL high-volumes of data with support for popular data sources, including file systems and object stores, relational and NoSQL databases, and SaaS systems.

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Earn a skill badge by completing the Create Conversational AI Agents with Dialogflow CX quest, where you will learn how to create a conversational virtual agent, including how to: define intents and entities, use versions and environments, create conversational branching, and use IVR features. 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 this skill badge quest, and the final assessment challenge lab, to receive a skill badge that you can share with your network.

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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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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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探索生成式 AI - Agent Platform”课程包含一系列实验， 指导用户在 Google Cloud 平台上使用生成式 AI。通过这些实验，您将了解 如何在 Agent Platform 中使用 Gemini 模型。您还将了解提示设计、最佳实践， 以及如何使用生成式 AI 进行构思、文本分类、文本提取、文本摘要等。此外， 您将学习如何通过 Agent Platform 中的自定义训练来训练基础模型， 从而对其进行调优，以及如何将其部署到 Agent Platform 中的端点。

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Building a Conversational Interface with Dialogflow CX Building conversational interfaces will enable you to engage your constituents in delightful new ways. This section will provide an overview of Dialogflow CX, which provides a range of new capabilities, languages, and functions. Scaling Equity and Access with Enterprise Translation Hub Accurate, timely and cost effective document translation has long been a challenge to providing equitable and accessible service to all constituents. In this section, explore Google's Enterprise Translation solutions which provide scalable document translation leveraging best in class Artificial Intelligence and Machine Learning, while retaining the critical human in the loop for final post editing review.

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本课程教您如何使用深度学习来创建图片标注模型。您将了解图片标注模型的不同组成部分，例如编码器和解码器，以及如何训练和评估模型。学完本课程，您将能够自行创建图片标注模型并用来生成图片说明。

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本课程向您介绍 Transformer 架构和 Bidirectional Encoder Representations from Transformers (BERT) 模型。您将了解 Transformer 架构的主要组成部分，例如自注意力机制，以及该架构如何用于构建 BERT 模型。您还将了解可以使用 BERT 的不同任务，例如文本分类、问答和自然语言推理。完成本课程估计需要大约 45 分钟。

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本课程简要介绍了编码器-解码器架构，这是一种功能强大且常见的机器学习架构，适用于机器翻译、文本摘要和问答等 sequence-to-sequence 任务。您将了解编码器-解码器架构的主要组成部分，以及如何训练和部署这些模型。在相应的实验演示中，您将在 TensorFlow 中从头编写简单的编码器-解码器架构实现代码，以用于诗歌生成。

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本课程将向您介绍注意力机制，这是一种强大的技术，可令神经网络专注于输入序列的特定部分。您将了解注意力的工作原理，以及如何使用它来提高各种机器学习任务的性能，包括机器翻译、文本摘要和问题解答。

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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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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。

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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 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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“Google Cloud 基础知识：核心基础设施”介绍在使用 Google Cloud 时会遇到的重要概念和术语。本课程通过视频和实操实验来介绍并比较 Google Cloud 的多种计算和存储服务，并提供重要的资源和政策管理工具。

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