加入 登录

在 Google Cloud 控制台中运用您的技能

Stephen Hwang

成为会员时间：2021

生成式 AI 簡介 Earned Jul 24, 2023 EDT
Serverless Data Processing with Dataflow: Foundations Earned Dec 1, 2021 EST
Building Resilient Streaming Analytics Systems on Google Cloud Earned Nov 30, 2021 EST
Smart Analytics, Machine Learning, and AI on Google Cloud Earned Nov 30, 2021 EST
Modernizing Data Lakes and Data Warehouses with Google Cloud Earned Nov 19, 2021 EST
在 Google Cloud 為機器學習 API 準備資料 Earned Nov 19, 2021 EST
Google Cloud Big Data and Machine Learning Fundamentals Earned Nov 9, 2021 EST
Google Cloud 基礎知識：核心基礎架構 Earned Nov 8, 2021 EST

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。

了解详情

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.

了解详情

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.

了解详情

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.

了解详情

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.

了解详情

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。

了解详情

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.

了解详情

「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。

了解详情