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Fernando Custodio dos Reis

成为会员时间：2022

青铜联赛

15935 积分
使用 Gemini 和 Streamlit 開發生成式 AI 應用程式 Earned Aug 25, 2024 EDT
Orchestrate LLM solutions with LangChain Earned Jan 26, 2024 EST
Migrate MySQL data to Cloud SQL using Database Migration Service Earned Jan 8, 2024 EST
透過 Google Cloud Observability 監控及記錄系統狀態 Earned Dec 20, 2023 EST
在 Google Cloud 設定應用程式開發環境 Earned Dec 4, 2023 EST
Generative AI Explorer : Vertex AI Earned Aug 5, 2023 EDT
大型語言模型簡介 Earned Jul 14, 2023 EDT
生成式 AI 簡介 Earned Jul 14, 2023 EDT
在 Compute Engine 實作負載平衡功能 Earned May 1, 2023 EDT
在 Google Cloud 為機器學習 API 準備資料 Earned May 1, 2023 EDT
Building Resilient Streaming Analytics Systems on Google Cloud - Locales Earned Jun 14, 2022 EDT
Building Resilient Streaming Analytics Systems on Google Cloud Earned Jun 2, 2022 EDT
Understanding LookML in Looker Earned Jun 1, 2022 EDT
Data Warehousing for Partners: Analyze Data with Looker Earned May 24, 2022 EDT
Serverless Data Processing with Dataflow: Foundations Earned May 11, 2022 EDT
使用 BigQuery ML 為預測模型進行資料工程 Earned May 4, 2022 EDT
[DEPRECATED] Data Engineering Earned May 4, 2022 EDT
Modernizing Data Lakes and Data Warehouses with Google Cloud Earned Apr 14, 2022 EDT
Inside Track: Dataflow Advanced Earned Apr 8, 2022 EDT
透過 BigQuery 建構資料倉儲 Earned Apr 4, 2022 EDT
BigQuery for Data Analysis I Earned Feb 24, 2022 EST

完成 使用 Gemini 和 Streamlit 開發生成式 AI 應用程式 技能徽章中階課程，即可證明您具備下列技能： 生成文字、透過 Python SDK 和 Gemini API 呼叫函式，以及運用 Cloud Run 部署 Streamlit 應用程式。 您將瞭解如何以不同方式透過提示請 Gemini 生成文字、使用 Cloud Shell 測試及疊代 Streamlit 應用程式，隨後封裝成 Docker 容器並在 Cloud Run 中部署。

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Learn to use LangChain to call Google Cloud LLMs and Generative AI Services and Datastores to simplify complex applications' code.

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Complete the introductory Migrate MySQL data to Cloud SQL using Database Migration Services skill badge to demonstrate skills in the following: migrating MySQL data to Cloud SQL using different job types and connectivity options available in Database Migration Service and migrating MySQL user data when running Database Migration Service jobs. 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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完成 透過 Google Cloud Observability 監控及記錄系統狀態 技能徽章入門課程， 即可證明您具備下列技能：監控 Compute Engine 中的虛擬機器、 運用 Cloud Monitoring 監管多項專案、在 Cloud Functions 延伸應用監控和記錄功能、 建立和傳送自訂應用程式指標，以及根據自訂指標設定 Cloud Monitoring 快訊。 「技能徽章」是 Google Cloud 核發的獨家數位徽章， 用於肯定您在 Google Cloud 產品與服務方面的精通程度， 代表您已通過測驗，能在互動式實作環境中應用相關知識。完成 本課程及結業評量挑戰研究室，即可取得技能徽章 並與親友分享。

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只要修完「在 Google Cloud 設定應用程式開發環境」課程，就能獲得技能徽章。 在本課程中，您將學會如何使用以下技術的基本功能，建構和連結以儲存空間為中心的雲端基礎架構：Cloud Storage、Identity and Access Management、Cloud Functions 和 Pub/Sub。 「技能徽章」是 Google Cloud 核發的獨家數位徽章，用於表彰您相當熟悉 Google Cloud 產品與服務，並已通過測驗，能在互動式實作環境中應用相關知識。只要完成這個技能徽章課程和最終評量挑戰研究室，即可取得技能徽章並與親友分享成就。

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This content is deprecated. Please see the latest version of the course, here.

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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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完成 在 Compute Engine 實作負載平衡功能 技能徽章入門課程，即可證明您具備下列技能： 編寫 gcloud 指令和使用 Cloud Shell、在 Compute Engine 建立及部署虛擬機器， 以及設定網路和 HTTP 負載平衡器。 「技能徽章」是 Google Cloud 核發的 獨家數位徽章，用於肯定您在 Google Cloud 產品與服務方面的精通程度， 代表您已通過測驗，能在互動式實作環境中應用相關 知識。完成這個課程及挑戰研究室 最終評量，即可取得技能徽章並與親友分享。

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完成 在 Google Cloud 為機器學習 API 準備資料 技能徽章入門課程，即可證明您具備下列技能： 使用 Dataprep by Trifacta 清理資料、在 Dataflow 執行資料管道、在 Dataproc 建立叢集和執行 Apache Spark 工作，以及呼叫機器學習 API，包含 Cloud Natural Language API、Google Cloud Speech-to-Text API 和 Video Intelligence API。 「技能徽章」是 Google Cloud 核發的獨家數位徽章，用於肯定您在 Google Cloud 產品與服務方面的精通程度， 代表您已通過測驗，能在互動式實作環境中應用相關知識。完成本技能徽章課程及結業評量挑戰研究室， 即可取得技能徽章並與他人分享。

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This course, Building Resilient Streaming Analytics Systems on Google Cloud - Locales, is intended for non-English learners. If you want to take this course in English, please enroll in Building Resilient Streaming Analytics Systems on Google Cloud. 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 will get hands-on experience building streaming data pipeline components on Google Cloud using QwikLabs.

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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 Bigtable for analysis. Learners get hands-on experience building streaming data pipeline components on Google Cloud by using QwikLabs.

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In this quest, you will get hands-on experience with LookML in Looker. You will learn how to write LookML code to create new dimensions and measures, create derived tables and join them to Explores, filter Explores, and define caching policies in LookML.

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This course explores how to leverage Looker to create data experiences and gain insights with modern business intelligence (BI) and reporting.

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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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完成使用 BigQuery ML 為預測模型進行資料工程技能徽章中階課程， 即可證明自己具備下列知識與技能：運用 Dataprep by Trifacta 建構連至 BigQuery 的資料轉換 pipeline； 使用 Cloud Storage、Dataflow 和 BigQuery 建構「擷取、轉換及載入」(ETL) 工作負載， 以及使用 BigQuery ML 建構機器學習模型。技能 徽章是 Google Cloud 核發的獨家數位徽章， 用於肯定您在 Google Cloud 產品和服務方面的精熟技能， 代表您已通過測驗，能在互動式實作環境中應用相關知識。完成 這個課程及結業評量挑戰實驗室，即可取得數位徽章 並與他人分享。

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This advanced-level quest is unique amongst the other catalog offerings. The labs have been curated to give IT professionals hands-on practice with topics and services that appear in the Google Cloud Certified Professional Data Engineer Certification. From Big Query, to Dataprep, to Cloud Composer, this quest is composed of specific labs that will put your Google Cloud data engineering knowledge to the test. Be aware that while practice with these labs will increase your skills and abilities, you will need other preparation, too. The exam is quite challenging and external studying, experience, and/or background in cloud data engineering is recommended. Looking for a hands on challenge lab to demonstrate your skills and validate your knowledge? On completing this quest, enroll in and finish the additional challenge lab at the end of the Engineer Data in the Google Cloud to receive an exclusive Google Cloud digital badge.

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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 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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完成 透過 BigQuery 建構資料倉儲 技能徽章中階課程，即可證明您具備下列技能： 彙整資料以建立新資料表、排解彙整作業問題、利用聯集附加資料、建立依日期分區的資料表， 以及在 BigQuery 使用 JSON、陣列和結構體。 「技能徽章」是 Google Cloud 核發的獨家數位徽章， 用於肯定您在 Google Cloud 產品和服務方面的精通程度， 代表您已通過測驗，能在互動式實作環境中應用相關 知識。完成技能徽章課程及結業評量挑戰研究室， 即可取得技能徽章並與他人分享。

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Welcome Gamers! Learn BigQuery and Cloud SQL, all while having fun! You will compete to see who can finish the game with the highest score. Earn the points by completing the steps in the lab.... and get bonus points for speed! Be sure to click "End" when you're done with each lab to get the maximum points. All players will be awarded the game badge.

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