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Chandan Gorai

成为会员时间：2022

白银联赛

7785 积分
Serverless Data Processing with Dataflow: Foundations Earned Dec 16, 2024 EST
Snowflake to BigQuery Migration Earned Dec 9, 2024 EST
Preparing for your Professional Cloud Architect Journey Earned Sep 17, 2024 EDT
在 Compute Engine 實作負載平衡功能 Earned Aug 22, 2023 EDT
Google Cloud Big Data and Machine Learning Fundamentals Earned Aug 5, 2023 EDT
Transformer 和 BERT 模型 Earned May 20, 2023 EDT
注意力機制 Earned May 20, 2023 EDT
大型語言模型簡介 Earned May 20, 2023 EDT
生成式 AI 簡介 Earned May 20, 2023 EDT
Preparing for Your Associate Cloud Engineer Journey Earned Jan 21, 2023 EST
開始使用 Google Kubernetes Engine Earned Nov 10, 2022 EST
DEPRECATED BigQuery Basics for Data Analysts Earned Nov 6, 2022 EST

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 workload aims to upskill Google Cloud partners to perform specific tasks associated with priority workloads. Learners will perform the tasks of migrating data from Snowflake to BigQuery. Sample data will be used during the migration. Learners will complete several labs that focus on the process of transferring schema, data and related processes to corresponding Google Cloud products.There will be one or more challenge labs that will test the learners' understanding of the topics. "This learning path aims to upskill Google Cloud partners to perform specific tasks associated with priority workloads. Learners will perform the tasks of migrating data from Snowflake to BigQuery.

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This course helps learners create a study plan for the PCA (Professional Cloud Architect) 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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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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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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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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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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This course helps you structure your preparation for the Associate Cloud Engineer exam. You will learn about the Google Cloud domains covered by the exam and how to create a study plan to improve your domain knowledge.

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歡迎參加「開始使用 Google Kubernetes Engine」課程。Kubernetes 是位於應用程式和硬體基礎架構之間的軟體層。如果您對這項技術感興趣，這堂課程可以滿足您的需求。有了 Google Kubernetes Engine，您就能在 Google Cloud 中以代管服務的形式使用 Kubernetes。 本課程的目標在於介紹 Google Kubernetes Engine (常簡稱為 GKE) 的基本概念，以及如何將應用程式容器化，以便在 Google Cloud 中執行。課程首先會初步介紹 Google Cloud，隨後簡介容器、Kubernetes、Kubernetes 架構和 Kubernetes 作業。

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Want to scale your data analysis efforts without managing database hardware? Learn the best practices for querying and getting insights from your data warehouse with this interactive series of BigQuery labs. BigQuery is Google's fully managed, NoOps, low cost analytics database. With BigQuery you can query terabytes and terabytes of data without having any infrastructure to manage or needing a database administrator. BigQuery uses SQL and can take advantage of the pay-as-you-go model. BigQuery allows you to focus on analyzing data to find meaningful insights.

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