kamesh negi
Member since 2023
Silver League
25545 points
Member since 2023
Hey there! You're invited to game on with the Arcade Trivia for October Week 1! Play throughout the month and boost your cloud learning journey. Every week, we'll release a new set of questions to test your knowledge of Google Cloud Platform. Get started now and earn the October Trivia Week 1 badge!
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。
各種規模的機構都開始運用雲端的強大功能和靈活性,徹底改變營運方式。不過,有效管理及擴充雲端資源可能是相當複雜的工作。本課程將探討在雲端中的現代營運、可靠性和韌性的基礎概念,以及 Google Cloud 如何協助您達成這些目標。本課程是 Cloud Digital Leader 學習路徑的一部分,旨在促進個人職能發展,並打造企業的未來。
雲端技術是強大的資產,與資料搭配後,將成為創新和提升客戶體驗的催化劑。「運用 Google Cloud 探索資料轉換功能」課程將探討機構如何運用雲端,讓資料更易於存取、做為行動依據和更有價值。 這門課程是 Cloud Digital Leader 學習路徑的一部分,旨在協助個人在職務上成長,並打造企業的未來。
數位轉型是現代組織必經的關鍵旅程,而建立穩固的雲端運算基礎,是跨出有意義創新的第一步。本課程「透過 Google Cloud 進行數位轉型」,將會介紹核心技術和策略框架,協助組織革新營運方式,並探討基本的雲端概念、全球網路基礎架構和共同責任模式,幫助主管在雲端之路上自信前行。本課程是 Cloud Digital Leader 學習路徑的一部分,旨在促進個人職能發展,並打造企業的未來。
瞭解如何在 Google Cloud 將原始數據轉化為明確的資料導向決策。本初級課程提供實作研究室和實際情境,引導您在 BigQuery 查詢資料、在 Looker 探索趨勢,以及透過數據分析工具製作可共用的互動式報表。
在本課程中,您將實際操作 Looker 的 LookML。您將學習如何編寫 LookML 程式碼,建立新的維度和測量指標、建立衍生資料表並彙整至探索、篩選探索,以及在 LookML 中定義快取政策。
完成 Looker 資料模型管理技能徽章中階課程,即可證明您具備下列技能:維護 LookML 專案的健全性、運用 SQL Runner 驗證資料、採用 LookML 最佳做法、改良查詢及 報表,並執行永久衍生資料表和快取政策。
完成「在 Looker 建構 LookML 物件」技能徽章入門課程, 即可證明您具備下列技能: 建立新的維度和測量指標、檢視畫面和衍生資料表;根據需求設定測量指標篩選器和類型; 更新維度和測量指標; 建構及調整「探索」;將檢視表彙整至現有「探索」;以及配合業務需求決定要建立哪些 LookML 物件。
完成「運用 BigQuery ML 建立機器學習模型」技能徽章中階課程,即可證明您具備下列技能: 可使用 BigQuery ML 建立及評估機器學習模型,並根據資料進行預測。
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.
在本課程中,您將透過實際操作,瞭解如何在 Looker 應用進階 LookerML 概念。您將學習如何使用 Liquid 自訂和建立動態維度 和測量指標、建構動態 SQL 衍生資料表和自訂的原生衍生資料表, 並運用擴充參數將 LookML 程式碼模組化。
完成 從 BigQuery 資料取得深入分析結果 技能徽章入門課程,即可證明您具備下列技能: 撰寫 SQL 查詢、查詢公開資料表、將樣本資料載入 BigQuery、使用 BigQuery 的查詢驗證工具 排解常見語法錯誤,以及在 Data Studio 中 透過連結 BigQuery 資料建立報表。
完成「為 Looker 資訊主頁和報表準備資料」技能徽章入門課程, 即可證明您具備下列技能:可篩選、排序和 pivot 資料、合併不同的 Looker 探索結果, 還能使用函式和運算子建構 Looker 資訊主頁和報表,取得資料分析結果和圖表。
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 Managed Service for Apache Spark 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。
This course empowers you to develop scalable, performant LookML (Looker Modeling Language) models that provide your business users with the standardized, ready-to-use data that they need to answer their questions. Upon completing this course, you will be able to start building and maintaining LookML models to curate and manage data in your organization’s Looker instance.
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.
In this course, we define what machine learning is and how it can benefit your business. You'll see a few demos of ML in action and learn key ML terms like instances, features, and labels. In the interactive labs, you will practice invoking the pretrained ML APIs available as well as build your own Machine Learning models using just SQL with BigQuery ML.
The third course in this course series is Achieving Advanced Insights with BigQuery. Here we will build on your growing knowledge of SQL as we dive into advanced functions and how to break apart a complex query into manageable steps. We will cover the internal architecture of BigQuery (column-based sharded storage) and advanced SQL topics like nested and repeated fields through the use of Arrays and Structs. Lastly we will dive into optimizing your queries for performance and how you can secure your data through authorized views. After completing this course, enroll in the Applying Machine Learning to your Data with Google Cloud course.
This is the second course in the Data to Insights course series. Here we will cover how to ingest new external datasets into BigQuery and visualize them with Looker Studio. We will also cover intermediate SQL concepts like multi-table JOINs and UNIONs which will allow you to analyze data across multiple data sources. Note: Even if you have a background in SQL, there are BigQuery specifics (like handling query cache and table wildcards) that may be new to you. After completing this course, enroll in the Achieving Advanced Insights with BigQuery course.
In this course, we see what the common challenges faced by data analysts are and how to solve them with the big data tools on Google Cloud. You’ll pick up some SQL along the way and become very familiar with using BigQuery and Dataprep to analyze and transform your datasets. This is the first course of the From Data to Insights with Google Cloud series. After completing this course, enroll in the Creating New BigQuery Datasets and Visualizing Insights course.
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.