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Patel Aarsh Miteshkumar

成为会员时间:2023

钻石联赛

41135 积分
Smart Analytics, Machine Learning, and AI on Google Cloud Earned Oct 26, 2024 EDT
Build Streaming Data Pipelines on Google Cloud Earned Oct 24, 2024 EDT
Build Batch Data Pipelines on Google Cloud Earned Oct 20, 2024 EDT
[DEPRECATED] Implementing Generative AI with Vertex AI Earned Oct 20, 2024 EDT
创建图片标注模型 Earned Oct 20, 2024 EDT
图像生成简介 Earned Oct 20, 2024 EDT
Transformer 模型和 BERT 模型 Earned Oct 20, 2024 EDT
编码器-解码器架构 Earned Oct 20, 2024 EDT
注意力机制 Earned Oct 20, 2024 EDT
Generative AI for Business Leaders Earned Oct 20, 2024 EDT
Serverless Data Processing with Dataflow: Operations Earned Oct 19, 2024 EDT
Serverless Data Processing with Dataflow: Foundations Earned Oct 19, 2024 EDT
Build Data Lakes and Data Warehouses on Google Cloud Earned Oct 19, 2024 EDT
Build a Certification Study Guide: PDE Exam Prep Earned Oct 19, 2024 EDT
在 Google Cloud 中管理 Kubernetes Earned Nov 1, 2023 EDT
使用 Security Command Center 消除威胁和漏洞 Earned Oct 31, 2023 EDT
Data Catalog Fundamentals Earned Oct 31, 2023 EDT
在 Google Cloud 上存储、处理和管理数据 - 控制台 Earned Oct 31, 2023 EDT
在 Google Cloud 上使用 TensorFlow 进行图片分类 Earned Oct 31, 2023 EDT
从 BigQuery 数据中挖掘数据洞见 Earned Oct 30, 2023 EDT
生成式 AI 简介 Earned Oct 30, 2023 EDT
Google Kubernetes Engine 使用入门 Earned Oct 30, 2023 EDT
在 Google Cloud 上为机器学习 API 准备数据 Earned Sep 13, 2023 EDT
在 Google Cloud 上设置应用开发环境 Earned Sep 13, 2023 EDT
构建安全的 Google Cloud 网络 Earned Sep 12, 2023 EDT
为 Compute Engine 实现云负载均衡 Earned Sep 12, 2023 EDT
基准:数据、机器学习和 AI Earned Sep 10, 2023 EDT
基准:基础架构 Earned Sep 9, 2023 EDT
Automate Deployment and Manage Traffic on a Google Cloud Network Earned Sep 9, 2023 EDT
Google Cloud 基础知识 Earned Sep 7, 2023 EDT

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 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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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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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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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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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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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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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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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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Learn how to use Gemini Notebook to create a personalized study guide for the Professional Data Engineer 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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完成在 Google Cloud 中管理 Kubernetes这一中级技能徽章课程, 展示您在以下方面的技能:使用 kubectl 管理部署、监控并 调试在 Google Kubernetes Engine (GKE) 上运行的应用,以及持续交付技术。

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完成中级技能徽章课程使用 Security Command Center 消除 威胁和漏洞,展示您在以下方面的技能: 预防和管理环境威胁、识别和缓解应用漏洞,以及应对安全异常。

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Data Catalog is deprecated and will be discontinued on January 30, 2026. You can still complete this course if you want to. For steps to transition your Data Catalog users, workloads, and content to Dataplex Catalog, see Transition from Data Catalog to Dataplex Catalog (https://cloud.google.com/dataplex/docs/transition-to-dataplex-catalog). Data Catalog is a fully managed and scalable metadata management service that empowers organizations to quickly discover, understand, and manage all of their data. In this quest you will start small by learning how to search and tag data assets and metadata with Data Catalog. After learning how to build your own tag templates that map to BigQuery table data, you will learn how to build MySQL, PostgreSQL, and SQLServer to Data Catalog Connectors.

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Cloud Storage, Cloud Functions 和 Cloud Pub/Sub 都是 Google Cloud Platform 服务,可用于存储、处理和管理数据。这三项 服务可结合使用,创建各种数据驱动型应用。在 此技能徽章课程中,您将使用 Cloud Storage 存储图片,使用 Cloud Functions 函数 处理图片,并使用 Cloud Pub/Sub 将图片发送到另一个应用。

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完成“在 Google Cloud 上使用 TensorFlow 进行图片分类”课程,赢取中级技能徽章 。在此课程中,您将学习如何使用 TensorFlow 和 Vertex AI 来创建和训练机器学习模型。您将主要使用 Vertex AI Workbench 上用户管理的 笔记本。

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完成入门级技能徽章课程“从 BigQuery 数据中挖掘数据洞见”,展示您在以下方面的技能: 编写 SQL 查询、查询公共表、将示例数据加载到 BigQuery 中、 在 BigQuery 中使用查询验证器排查常见的语法错误,以及通过连接到 BigQuery 数据在 Data Studio 中 创建报告。

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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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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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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 Managed Service for Apache Spark 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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完成“在 Google Cloud 上设置应用开发环境”课程,赢取技能徽章;通过该课程,您将了解如何使用以下技术的基本功能来构建和连接以存储为中心的云基础设施: Cloud Storage、Identity and Access Management、Cloud Functions 和 Pub/Sub。

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完成构建安全的 Google Cloud 网络课程,赢取技能徽章。在此课程中,您将了解与网络有关的众多 资源,以便在 Google Cloud 上构建、扩缩和保护自己的应用。

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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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大数据、机器学习和人工智能是当今计算领域的热门话题, 但这些领域的专业性很强,因而很难找到 入门资料。幸运的是,Google Cloud 在这些领域提供了方便用户使用的服务, 通过本入门级课程,您可以 开始学习使用 BigQuery、Cloud Speech API 和 Video Intelligence 等工具。

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如果您是一位入门级云开发者, 在学习了“Google Cloud 基础知识”课程之后,想要寻求真正的实操机会,这门课程就是您的不二之选。您将获得宝贵的实操经验, 通过多个实验深入探索 Cloud Storage 以及 Monitoring 和 Cloud Functions 等其他关键应用服务。您将掌握一系列宝贵技能, 在 Google Cloud 的任何计划中,这些技能都能发挥作用。

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Networking is a principle theme of cloud computing. It’s the underlying structure of Google Cloud, and it’s what connects all your resources and services to one another. This course will cover essential Google Cloud networking services and will give you hands-on practice with specialized tools for developing mature networks. From learning the ins-and-outs of VPCs, to creating enterprise-grade load balancers, Automate Deployment and Manage Traffic on a Google Cloud Network will give you the practical experience needed so you can start building robust networks right away.

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在本入门级课程中,您将了解 Google Cloud 的基础工具和服务。此课程提供了可选视频, 旨在帮助您深入了解和回顾实验中涉及的概念。Google Cloud 基础知识是推荐给 Google Cloud 学员的第一门课程 - 即使您几乎没有云相关知识,也能从中获得实践 经验,并将其直接运用于您的首个 Google Cloud 项目。从编写 Cloud Shell 命令和部署您的第一个虚拟机,到在 Kubernetes Engine 上运行应用 或者使用负载均衡,“Google Cloud 基础知识”都是您了解该平台 基本功能的首选入门级课程。

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