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Carlos Guerreiro

成为会员时间：2023

黄金联赛

15545 积分
Prepare Data for ML APIs on Google Cloud徽章 Prepare Data for ML APIs on Google Cloud Earned Nov 25, 2023 EST
Google Cloud Fundamentals: Core Infrastructure - 繁體中文徽章 Google Cloud Fundamentals: Core Infrastructure - 繁體中文 Earned Nov 19, 2023 EST
Cloud Functions: 3 Ways徽章 Cloud Functions: 3 Ways Earned Nov 19, 2023 EST
BigQuery Fundamentals for Redshift Professionals徽章 BigQuery Fundamentals for Redshift Professionals Earned Oct 14, 2023 EDT
Set Up an App Dev Environment on Google Cloud徽章 Set Up an App Dev Environment on Google Cloud Earned Oct 8, 2023 EDT
Optimize Costs for Google Kubernetes Engine徽章 Optimize Costs for Google Kubernetes Engine Earned Oct 7, 2023 EDT
Implement Cloud Security Fundamentals on Google Cloud徽章 Implement Cloud Security Fundamentals on Google Cloud Earned Sep 23, 2023 EDT
Analyzing and Visualizing Data in Looker徽章 Analyzing and Visualizing Data in Looker Earned Sep 17, 2023 EDT
Logging and Monitoring in Google Cloud徽章 Logging and Monitoring in Google Cloud Earned Sep 16, 2023 EDT
Generative AI Explorer - Vertex AI徽章 Generative AI Explorer - Vertex AI Earned Sep 2, 2023 EDT
Reliable Google Cloud Infrastructure: Design and Process - 繁體中文徽章 Reliable Google Cloud Infrastructure: Design and Process - 繁體中文 Earned Aug 26, 2023 EDT
Responsible AI: Applying AI Principles with Google Cloud - 繁體中文徽章 Responsible AI: Applying AI Principles with Google Cloud - 繁體中文 Earned Aug 26, 2023 EDT
Launching into Machine Learning徽章 Launching into Machine Learning Earned Jun 27, 2023 EDT
How Google Does Machine Learning徽章 How Google Does Machine Learning Earned Jun 14, 2023 EDT
Introduction to Generative AI Studio - 繁體中文徽章 Introduction to Generative AI Studio - 繁體中文 Earned Jun 9, 2023 EDT
Create Image Captioning Models - 繁體中文徽章 Create Image Captioning Models - 繁體中文 Earned Jun 9, 2023 EDT
Transformer Models and BERT Model - 繁體中文徽章 Transformer Models and BERT Model - 繁體中文 Earned Jun 9, 2023 EDT
Attention Mechanism - 繁體中文徽章 Attention Mechanism - 繁體中文 Earned Jun 9, 2023 EDT
Encoder-Decoder Architecture - 繁體中文徽章 Encoder-Decoder Architecture - 繁體中文 Earned Jun 8, 2023 EDT
Introduction to Image Generation - 繁體中文徽章 Introduction to Image Generation - 繁體中文 Earned Jun 8, 2023 EDT
Generative AI Fundamentals - 繁體中文徽章 Generative AI Fundamentals - 繁體中文 Earned Jun 8, 2023 EDT
Introduction to Responsible AI - 繁體中文徽章 Introduction to Responsible AI - 繁體中文 Earned Jun 8, 2023 EDT
Introduction to Large Language Models - 繁體中文徽章 Introduction to Large Language Models - 繁體中文 Earned Jun 8, 2023 EDT
Introduction to Generative AI - 繁體中文徽章 Introduction to Generative AI - 繁體中文 Earned Jun 8, 2023 EDT
Serverless Data Processing with Dataflow: Foundations徽章 Serverless Data Processing with Dataflow: Foundations Earned Jun 1, 2023 EDT
Smart Analytics, Machine Learning, and AI on Google Cloud徽章 Smart Analytics, Machine Learning, and AI on Google Cloud Earned May 31, 2023 EDT
Building Resilient Streaming Analytics Systems on Google Cloud徽章 Building Resilient Streaming Analytics Systems on Google Cloud Earned May 27, 2023 EDT
Building Batch Data Pipelines on Google Cloud徽章 Building Batch Data Pipelines on Google Cloud Earned May 26, 2023 EDT
Preparing for your Professional Data Engineer Journey徽章 Preparing for your Professional Data Engineer Journey Earned May 25, 2023 EDT
Modernizing Data Lakes and Data Warehouses with Google Cloud徽章 Modernizing Data Lakes and Data Warehouses with Google Cloud Earned May 24, 2023 EDT
Google Cloud Big Data and Machine Learning Fundamentals徽章 Google Cloud Big Data and Machine Learning Fundamentals Earned May 23, 2023 EDT
Build Google Cloud Infrastructure for AWS Professionals徽章 Build Google Cloud Infrastructure for AWS Professionals Earned May 21, 2023 EDT
Deploy and Monitor in Google Cloud for AWS Professionals徽章 Deploy and Monitor in Google Cloud for AWS Professionals Earned May 21, 2023 EDT
Google Cloud Storage and Containers for AWS Professionals徽章 Google Cloud Storage and Containers for AWS Professionals Earned May 21, 2023 EDT
Google Cloud Compute and Scalability for AWS Professionals徽章 Google Cloud Compute and Scalability for AWS Professionals Earned May 20, 2023 EDT
Google Cloud IAM and Networking for AWS Professionals徽章 Google Cloud IAM and Networking for AWS Professionals Earned May 19, 2023 EDT

Complete the introductory Prepare Data for ML APIs on Google Cloud skill badge to demonstrate skills in the following: cleaning data with Dataprep by Trifacta, running data pipelines in Dataflow, creating clusters and running Apache Spark jobs in Dataproc, and calling ML APIs including the Cloud Natural Language API, Google Cloud Speech-to-Text API, and Video Intelligence API. 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 course, and the final assessment challenge lab, to receive a skill badge that you can share with your network.

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Google Cloud 基礎知識：「核心基礎架構」介紹了在使用 Google Cloud 時會遇到的重要概念和術語。本課程會透過影片和實作研究室，介紹並比較 Google Cloud 的多種運算和儲存服務，同時提供重要的資源和政策管理工具。

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Earn a skill badge by completing the Cloud Functions: 3 Ways course, where you learn how to use Cloud Functions (including 2nd gen) through the Google Cloud console and on the command line. 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, and the final assessment challenge lab, to receive a digital badge that you can share with your network.

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This course covers BigQuery fundamentals for professionals who are familiar with SQL-based cloud data warehouses in Redshift and want to begin working in BigQuery. Through interactive lecture content and hands-on labs, you learn how to provision resources, create and share data assets, ingest data, and optimize query performance in BigQuery. Drawing upon your knowledge of Redshift, you also learn about similarities and differences between Redshift and BigQuery to help you get started with data warehouses in BigQuery. After this course, you can continue your BigQuery journey by completing the skill badge quest titled Build and Optimize Data Warehouses with BigQuery.

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Earn a skill badge by completing the Set Up an App Dev Environment on Google Cloud course, where you learn how to build and connect storage-centric cloud infrastructure using the basic capabilities of the of the following technologies: Cloud Storage, Identity and Access Management, Cloud Functions, and Pub/Sub. 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, and the final assessment challenge lab, to receive a skill badge that you can share with your network.

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Complete the intermediate Optimize Costs for Google Kubernetes Engine skill badge to demonstrate skills in the following: creating and managing multi-tenant clusters, monitoring resource usage by namespace, configuring cluster and pod autoscaling for efficiency, setting up load balancing for optimal resource distribution, and implementing liveness and readiness probes to ensure application health and cost-effectiveness. 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 course and the final assessment challenge lab to receive a skill badge that you can share with your network.

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Complete the intermediate Implement Cloud Security Fundamentals on Google Cloud skill badge to demonstrate skills in the following: creating and assigning roles with Identity and Access Management (IAM); creating and managing service accounts; enabling private connectivity across virtual private cloud (VPC) networks; restricting application access using Identity-Aware Proxy; managing keys and encrypted data using Cloud Key Management Service (KMS); and creating a private Kubernetes cluster. 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 skill badge course, and final assessment challenge lab, to receive a digital badge that you can share with your network.

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In this course, you learn how to do the kind of data exploration and analysis in Looker that would formerly be done primarily by SQL developers or analysts. Upon completion of this course, you will be able to leverage Looker's modern analytics platform to find and explore relevant content in your organization’s Looker instance, ask questions of your data, create new metrics as needed, and build and share visualizations and dashboards to facilitate data-driven decision making.

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This course teaches participants techniques for monitoring and improving infrastructure and application performance in Google Cloud. Using a combination of presentations, demos, hands-on labs, and real-world case studies, attendees gain experience with full-stack monitoring, real-time log management and analysis, debugging code in production, tracing application performance bottlenecks, and profiling CPU and memory usage.

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The Generative AI Explorer - Vertex Quest is a collection of labs on how to use Generative AI on Google Cloud. Through the labs, you will learn about how to use the models in the Vertex AI PaLM API family, including text-bison, chat-bison, and textembedding-gecko. You will also learn about prompt design, best practices, and how it can be used for ideation, text classification, text extraction, text summarization, and more. You will also learn how to tune a foundation model by training it via Vertex AI custom training and deploy it to a Vertex AI endpoint.

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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，進而設計出相當可靠、高可用性、安全又符合成本效益的 Google Cloud 部署項目。

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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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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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本課程將介紹 Vertex AI 上的 Generative AI Studio，說明如何用此產品設計生成式 AI 模型的原型及自訂生成式 AI 模型，以利您在應用程式中使用模型的功能。課程中，您可以透過 Generative AI Studio 示範教學逐步認識 Generative AI Studio，並瞭解產品功能、選項與使用方式。課程最後，您可以在實作研究室應用所學，並進行測驗來評估學習成果。

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本課程說明如何使用深度學習來建立圖像說明生成模型。您將學習圖像說明生成模型的各個不同組成部分，例如編碼器和解碼器，以及如何訓練和評估模型。在本課程結束時，您將能建立自己的圖像說明生成模型，並使用模型產生圖像說明文字。

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這堂課程將說明變換器架構，以及基於變換器的雙向編碼器表示技術 (BERT) 模型，同時帶您瞭解變換器架構的主要組成 (如自我注意力機制) 和如何用架構建立 BERT 模型。此外，也會介紹 BERT 適用的各種任務，像是文字分類、問題回答和自然語言推論。課程預計約 45 分鐘。

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本課程將介紹注意力機制，說明這項強大技術如何讓類神經網路專注於輸入序列的特定部分。此外，也將解釋注意力的運作方式，以及如何使用注意力來提高各種機器學習任務的成效，包括機器翻譯、文字摘要和回答問題。

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本課程概要說明解碼器與編碼器的架構，這種強大且常見的機器學習架構適用於序列對序列的任務，例如機器翻譯、文字摘要和回答問題。您將認識編碼器與解碼器架構的主要元件，並瞭解如何訓練及提供這些模型。在對應的研究室逐步操作說明中，您將學習如何從頭開始使用 TensorFlow 寫程式，導入簡單的編碼器與解碼器架構來產生詩詞。

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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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完成「Introduction to Generative AI」、「Introduction to Large Language Models」和「Introduction to Responsible AI」課程，即可獲得技能徽章。通過最終測驗，就能展現您對生成式 AI 基本概念的掌握程度。 「技能徽章」是 Google Cloud 核發的數位徽章，用於表彰您對 Google Cloud 產品和服務的相關知識。您可以將技能徽章公布在社群媒體的個人資料中，向其他人分享您的成果。

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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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這堂入門微學習課程說明大型語言模型 (LLM) 的定義與用途，以及如何調整提示來提高 LLM 成效。這堂課程也會介紹多項 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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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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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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Processing streaming data is becoming increasingly popular as streaming enables businesses to get real-time metrics on business operations. This course covers how to build streaming data pipelines on Google Cloud. Pub/Sub is described for handling incoming streaming data. The course also covers how to apply aggregations and transformations to streaming data using Dataflow, and how to store processed records to BigQuery or Cloud Bigtable for analysis. Learners get hands-on experience building streaming data pipeline components on Google Cloud by using QwikLabs.

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Data pipelines typically fall under one of the Extract and Load (EL), Extract, Load and Transform (ELT) or Extract, Transform and Load (ETL) paradigms. This course describes which paradigm should be used and when for batch data. Furthermore, this course covers several technologies on Google Cloud for data transformation including BigQuery, executing Spark on Dataproc, pipeline graphs in Cloud Data Fusion and serverless data processing with Dataflow. Learners get hands-on experience building data pipeline components on Google Cloud using Qwiklabs.

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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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The two key components of any data pipeline are data lakes and warehouses. This course highlights use-cases for each type of storage and dives into the available data lake and warehouse solutions on Google Cloud in technical detail. Also, this course describes the role of a data engineer, the benefits of a successful data pipeline to business operations, and examines why data engineering should be done in a cloud environment. This is the first course of the Data Engineering on Google Cloud series. After completing this course, enroll in the Building Batch Data Pipelines on Google Cloud course.

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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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Earn a skill badge by completing the Build Google Cloud Infrastructure for AWS Professionals course, where you learn how to configure IAM permission, orchestrate workloads using Kubernetes, host a web application using compute engine, and configure load balancing. 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, and the final assessment challenge lab, to receive a digital badge that you can share with your network.

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This is the fourth course of a four-course series for cloud architects and engineers with existing AWS knowledge. It compares Google Cloud and AWS solutions and guides professionals on their use. This course focuses on deploying and monitoring applications in Google Cloud. The learners apply the knowledge of monitoring and application deployment processes in AWS to explore the differences with Google Cloud. Learners get hands-on practice building and managing Google Cloud resources.

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This is the third course of a four-course series for cloud architects and engineers with existing AWS knowledge, and it compares Google Cloud and AWS solutions and guides professionals on their use. This course focuses on Storage Options and containers in Google Cloud. The learners apply the knowledge of storage and containers in AWS to explore the similarities and differences with storage and containers in Google Cloud. Learners get hands-on practice building and managing Google Cloud resources.

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This is the second course of a four-course series for cloud architects and engineers with existing AWS knowledge. It aims to compare Google Cloud and AWS solutions and guide professionals on their use. This course focuses on compute resources and load balancing in Google Cloud. The learner will apply the knowledge of using virtual machines and load balancers in AWS to explore the similarities and differences with configuring and managing compute resources and load balancers in Google Cloud. Learners will get hands-on practice building and managing Google Cloud resources.

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This is the first course of a four-course series for cloud architects and engineers with existing AWS knowledge, and it compares Google Cloud and AWS solutions and guides professionals on their use. This course focuses on Identity and Access Management (IAM) and networking in Google Cloud. The learners apply the knowledge of access management and networking in AWS to explore the similarities and differences with access management and networking in Google Cloud. Learners get hands-on practice building and managing Google Cloud resources.

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