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Pravesh V Prasad

Member since 2020

Silver League

7585 points
Google Cloud IAM and Networking for Azure Professionals Earned Jul 13, 2025 EDT
Build Streaming Data Pipelines on Google Cloud Earned Jun 13, 2025 EDT
重要的 Google Cloud 基礎架構：核心服務 Earned Jun 13, 2025 EDT
Google Cloud Platform Fundamentals: Core Infrastructure Earned Jun 13, 2025 EDT
在 Google Cloud 設定應用程式開發環境 Earned Jun 9, 2021 EDT
在 Compute Engine 導入 Cloud Load Balancing Earned Jun 6, 2021 EDT
Elastic Google Cloud Infrastructure: Scaling and Automation - Locales Earned May 30, 2021 EDT
重要的 Google Cloud 基礎架構：基本概念 Earned May 23, 2021 EDT
DEPRECATED Applying BigQuery ML's Classification, Regression, and Demand Forecasting for Retail Applications Earned May 16, 2021 EDT
運用 BigQuery ML 建立機器學習模型 Earned May 15, 2021 EDT
使用 BigQuery 進行機器學習 Earned May 15, 2021 EDT
Smart Analytics, Machine Learning, and AI on Google Cloud Earned May 9, 2021 EDT
Build Batch Data Pipelines on Google Cloud Earned Apr 4, 2021 EDT
Build Data Lakes and Data Warehouses on Google Cloud Earned Feb 28, 2021 EST
[DEPRECATED] Google Cloud Big Data and Machine Learning Fundamentals Earned Jan 17, 2021 EST

This is the first course of a four-course series for cloud architects and engineers with existing Azure knowledge, and it compares Google Cloud and Azure 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 Azure 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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In this course you will get hands-on in order to work through real-world challenges faced when building streaming data pipelines. The primary focus is on managing continuous, unbounded data with Google Cloud products.

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

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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。

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完成「在 Compute Engine 導入 Cloud Load Balancing」技能徽章入門課程，即可證明您具備下列技能： 在 Compute Engine 建立及部署虛擬機器， 以及設定網路和應用程式負載平衡器。

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This course version is for non-English only. If you wish to take this course in English, please enroll here: Elastic Google Cloud Infrastructure: Scaling and Automation. If you wish to take it in another language, change your language in settings to see availability.

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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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In this course you will learn how to use several BigQuery ML features to improve retail use cases. Predict the demand for bike rentals in NYC with demand forecasting, and see how to use BigQuery ML for a classification task that predicts the likelihood of a website visitor making a purchase.

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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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不想花費大把時間，想在幾分鐘內只靠 SQL，就建立好機器學習模型嗎？透過 BigQuery ML，資料分析師可以運用現有的 SQL 工具和技巧，建立、訓練、評估模型， 並使用模型進行預測，降低機器學習的使用門檻。在 本系列的實驗室，您會測試不同類型的模型，瞭解 優良模型應具備的條件。

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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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In this intermediate course, you will learn to design, build, and optimize robust batch data pipelines on Google Cloud. Moving beyond fundamental data handling, you will explore large-scale data transformations and efficient workflow orchestration, essential for timely business intelligence and critical reporting. Get hands-on practice using Dataflow for Apache Beam and Serverless for Apache Spark (Dataproc Serverless) for implementation, and tackle crucial considerations for data quality, monitoring, and alerting to ensure pipeline reliability and operational excellence. A basic knowledge of data warehousing, ETL/ELT, SQL, Python, and Google Cloud concepts is recommended.

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While the traditional approaches of using data lakes and data warehouses can be effective, they have shortcomings, particularly in large enterprise environments. This course introduces the concept of a data lakehouse and the Google Cloud products used to create one. A lakehouse architecture uses open-standard data sources and combines the best features of data lakes and data warehouses, which addresses many of their shortcomings.

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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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