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Edgar Covantes Osuna

Member since 2022

Gold League

44950 points
瞭解 Looker 中的 LookML Earned Jun 3, 2024 EDT
Looker 資料模型管理 Earned May 12, 2024 EDT
在 Looker 建構 LookML 物件 Earned May 11, 2024 EDT
Data Catalog Fundamentals Earned May 10, 2024 EDT
在 Looker 應用進階 LookML 概念 Earned May 9, 2024 EDT
運用 BigQuery ML 建立機器學習模型 Earned May 8, 2024 EDT
從 BigQuery 資料取得深入分析結果 Earned May 8, 2024 EDT
為 Looker 資訊主頁和報表準備資料 Earned May 7, 2024 EDT
Developing Data Models with LookML Earned May 4, 2024 EDT
在 Google Cloud 為機器學習 API 準備資料 Earned May 2, 2024 EDT
Analyzing and Visualizing Data in Looker Earned May 2, 2024 EDT
Google Cloud 資料分析基本概念 Earned Apr 30, 2024 EDT
透過 Vertex AI 建構及部署機器學習解決方案 Earned Feb 6, 2024 EST
ML Pipelines on Google Cloud Earned Feb 6, 2024 EST
MLOps with Agent Platform: Manage Features Earned Jan 19, 2024 EST
Machine Learning Operations (MLOps): Getting Started Earned Jan 19, 2024 EST
Recommendation Systems on Google Cloud Earned Jan 17, 2024 EST
Natural Language Processing on Google Cloud Earned Jan 12, 2024 EST
Computer Vision Fundamentals with Google Cloud Earned Jan 5, 2024 EST
Machine Learning in the Enterprise Earned Jan 3, 2024 EST
Production Machine Learning Systems Earned Dec 28, 2023 EST
[DEPRECATED] Feature Engineering Earned Dec 13, 2023 EST
[Depricated] Build, Train and Deploy ML Models with Keras on Google Cloud Earned Dec 2, 2023 EST
Launching into Machine Learning Earned Nov 18, 2023 EST
Google Cloud 的 AI 和機器學習服務簡介 Earned Oct 30, 2023 EDT
在 Google Cloud 為機器學習 API 準備資料 Earned Sep 30, 2023 EDT
重要的 Google Cloud 基礎架構：核心服務 Earned Jul 29, 2023 EDT
彈性的 Google Cloud 基礎架構：資源調度與自動化 Earned Jul 29, 2023 EDT
重要的 Google Cloud 基礎架構：基本概念 Earned Jul 28, 2023 EDT
Google Cloud 基礎知識：核心基礎架構 Earned Jul 27, 2023 EDT
Google Cloud 運算的基本概念：Google Cloud 中的資料、機器學習和 AI Earned Nov 30, 2022 EST
Google Cloud 運算的基本概念：Google Cloud 的網路與安全性 Earned Nov 29, 2022 EST
Google Cloud 運算的基本概念：Google Cloud 基礎架構 Earned Nov 28, 2022 EST
Google Cloud 運算的基本概念：Cloud 運算基礎知識 Earned Nov 19, 2022 EST

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。

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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。

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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。

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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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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。

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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。

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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。

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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。

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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.

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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 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。

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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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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。

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完成 透過 Vertex AI 建構及部署機器學習解決方案 課程，即可瞭解如何使用 Google Cloud 的 Vertex AI 平台、AutoML 和自訂訓練服務， 訓練、評估、調整、解釋及部署機器學習模型。 這個技能徽章課程適合專業數據資料學家和機器學習 工程師，完成即可取得中階技能徽章。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應用相關知識。完成這個技能徽章課程 和結業評量挑戰實驗室，就能獲得數位徽章， 並與親友分享。

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In this course, you will be learning from ML Engineers and Trainers who work with the state-of-the-art development of ML pipelines here at Google Cloud. The first few modules will cover about TensorFlow Extended (or TFX), which is Google’s production machine learning platform based on TensorFlow for management of ML pipelines and metadata. You will learn about pipeline components and pipeline orchestration with TFX. You will also learn how you can automate your pipeline through continuous integration and continuous deployment, and how to manage ML metadata. Then we will change focus to discuss how we can automate and reuse ML pipelines across multiple ML frameworks such as tensorflow, pytorch, scikit learn, and xgboost. You will also learn how to use another tool on Google Cloud, Cloud Composer, to orchestrate your continuous training pipelines. And finally, we will go over how to use MLflow for managing the complete machine learning life cycle.

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This course introduces participants to MLOps tools and best practices for deploying, evaluating, monitoring and operating production ML systems on Google Cloud. MLOps is a discipline focused on the deployment, testing, monitoring, and automation of ML systems in production. Learners will get hands-on practice using Agent Platform Feature Store's streaming ingestion at the SDK layer.

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This course introduces participants to MLOps tools and best practices for deploying, evaluating, monitoring and operating production ML systems on Google Cloud. MLOps is a discipline focused on the deployment, testing, monitoring, and automation of ML systems in production. Machine Learning Engineering professionals use tools for continuous improvement and evaluation of deployed models. They work with (or can be) Data Scientists, who develop models, to enable velocity and rigor in deploying the best performing models.

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In this course, you apply your knowledge of classification models and embeddings to build a ML pipeline that functions as a recommendation engine. This is the fifth and final course of the Advanced Machine Learning on Google Cloud series.

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This course introduces the products and solutions to solve NLP problems on Google Cloud. Additionally, it explores the processes, techniques, and tools to develop an NLP project with neural networks by using Vertex AI and TensorFlow.

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This course describes different types of computer vision use cases and then highlights different machine learning strategies for solving these use cases. The strategies vary from experimenting with pre-built ML models through pre-built ML APIs and AutoML Vision to building custom image classifiers using linear models, deep neural network (DNN) models or convolutional neural network (CNN) models. The course shows how to improve a model's accuracy with augmentation, feature extraction, and fine-tuning hyperparameters while trying to avoid overfitting the data. The course also looks at practical issues that arise, for example, when one doesn't have enough data and how to incorporate the latest research findings into different models. Learners will get hands-on practice building and optimizing their own image classification models on a variety of public datasets in the labs they will work on.

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This course takes a real-world approach to the ML Workflow through a case study. An ML team faces several ML business requirements and use cases. The team must understand the tools required for data management and governance and consider the best approach for data preprocessing. The team is presented with three options to build ML models for two use cases. The course explains why they would use AutoML, BigQuery ML, or custom training to achieve their objectives.

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This course covers how to implement the various flavors of production ML systems— static, dynamic, and continuous training; static and dynamic inference; and batch and online processing. You delve into TensorFlow abstraction levels, the various options for doing distributed training, and how to write distributed training models with custom estimators. This is the second course of the Advanced Machine Learning on Google Cloud series. After completing this course, enroll in the Image Understanding with TensorFlow on Google Cloud course.

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This course explores the benefits of using Vertex AI Feature Store, how to improve the accuracy of ML models, and how to find which data columns make the most useful features. This course also includes content and labs on feature engineering using BigQuery ML, Keras, and TensorFlow.

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This course covers building ML models with TensorFlow and Keras, improving the accuracy of ML models and writing ML models for scaled use.

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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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本課程介紹 Google Cloud 的 AI 和機器學習 (ML) 功能，著重說明如何開發生成式和預測式 AI 專案。我們也會探討「從資料到 AI」整個生命週期都適用的技術、產品和工具，並透過互動式練習，協助資料科學家、AI 開發人員和機器學習工程師精進專業知識。

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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 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。

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這堂隨選密集課程會向參加人員說明 Google Cloud 提供的全方位彈性基礎架構和平台服務，並將重點放在 Compute Engine。這堂課程結合了視訊講座、示範和實作研究室，可讓參加人員探索及部署解決方案元素，例如網路、系統和應用程式服務等基礎架構元件。另外，這堂課也會介紹如何部署實用的解決方案，包括客戶提供的加密金鑰、安全性和存取權管理機制、配額與帳單，以及資源監控功能。

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這堂隨選密集課程會向參加人員說明 Google Cloud 提供的全方位彈性基礎架構和平台服務。這堂課結合了視訊講座、示範和實作研究室，可讓參加人員探索及部署解決方案元素，包括安全地建立互連網路、負載平衡、自動調度資源、基礎架構自動化，以及代管服務。

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這堂隨選密集課程會向參加人員說明 Google Cloud 提供的全方位彈性基礎架構和平台服務，尤其側重於 Compute Engine。這堂課程結合了視訊講座、示範和實作研究室，可讓參加人員探索及部署解決方案元素，例如網路、虛擬機器和應用程式服務等基礎架構元件。您會瞭解如何透過控制台和 Cloud Shell 使用 Google Cloud。另外，您也能瞭解雲端架構師的職責、基礎架構設計方法，以及具備虛擬私有雲 (VPC)、專案、網路、子網路、IP 位址、路徑和防火牆規則的虛擬網路設定。

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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實驗室，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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Google Cloud 運算基本概念課程，適合幾乎沒有雲端運算背景或經驗的學員。這些課程會概略介紹雲端基礎知識、大數據和機器學習的核心概念，以及 Google Cloud 的角色和定位。完成這一系列課程後，學員將能闡述這些概念並展示實用技能。學員需依序完成課程： 1. Google Cloud 運算的基本概念：Cloud 運算基礎知識 2. Google Cloud 運算的基本概念：Google Cloud 基礎架構 3. Google Cloud 運算的基本概念：Google Cloud 的網路與安全性 4. Google Cloud 運算的基本概念：Google Cloud 中的資料、機器學習和 AI 本系列的最後一堂課回顧了代管大數據服務、機器學習與這項技術的價值，以及如何獲得技能徽章，進一步展示您的 Google Cloud 技能。

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Google Cloud 運算基本概念課程，適合幾乎沒有雲端運算背景或經驗的學員。這些課程會說明雲端運算基本知識、大數據和機器學習的核心概念，以及 Google Cloud 的角色和定位。 完成這一系列課程後，學員將能夠闡述這些概念並展示實用技能。學員應依以下順序完成課程： 1. Google Cloud 運算的基本概念：Cloud 運算基礎知識 2. Google Cloud 運算的基本概念：Google Cloud 基礎架構 3. Google Cloud 運算的基本概念：Google Cloud 的網路與安全性 4. Google Cloud 運算的基本概念：Google Cloud 中的資料、機器學習和 AI 第三門課涵蓋雲端自動化和管理工具，以及建構安全網路。

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Google Cloud 運算基本概念課程，適合幾乎沒有雲端運算背景或經驗的學員。這些課程會概略介紹雲端基礎知識、大數據和機器學習的核心概念，以及 Google Cloud 的角色和定位。完成這一系列課程後，學員將能闡述這些概念並展示實用技能。學員需依序完成課程： 1. Google Cloud 運算的基本概念：Cloud 運算基礎知識 2. Google Cloud 運算的基本概念：Google Cloud 基礎架構 3. Google Cloud 運算的基本概念：Google Cloud 的網路與安全性 4. Google Cloud 運算的基本概念：Google Cloud 中的資料、機器學習和 AI

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Google Cloud 運算基本概念課程，適合幾乎沒有雲端運算背景或經驗的學員。這些課程會說明雲端運算基本知識、大數據和機器學習的核心概念，以及 Google Cloud 的角色和定位。完成這一系列課程後，學員將能夠闡述這些概念並展示實用技能。學員應依以下順序完成課程： 1. Google Cloud 運算的基本概念：Cloud 運算基礎知識 2. Google Cloud 運算的基本概念：Google Cloud 基礎架構 3. Google Cloud 運算的基本概念：Google Cloud 的網路與安全性 4. Google Cloud 運算的基本概念：Google Cloud 中的資料、機器學習和 AI 第一門課會概略說明雲端運算、Google Cloud 的使用方式，以及不同的運算選項。

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