Build, Train and Deploy ML Models with Keras on Google Cloud
Earned Jul 16, 2024 EDT
[CEPF L300 Course]: Data Analytics
Earned May 31, 2024 EDT
Launching into Machine Learning
Earned Apr 11, 2024 EDT
Google Cloud 的 AI 和機器學習服務簡介
Earned Jan 22, 2024 EST
Serverless Data Processing with Dataflow: Foundations
Earned Dec 18, 2023 EST
Smart Analytics, Machine Learning, and AI on Google Cloud
Earned Dec 16, 2023 EST
Build Streaming Data Pipelines on Google Cloud
Earned Dec 14, 2023 EST
Google Cloud Big Data and Machine Learning Fundamentals
Earned Sep 30, 2023 EDT
探索生成式 AI - Agent Platform
Earned Aug 3, 2023 EDT
Preparing for your Professional Data Engineer Journey
Earned Jul 12, 2023 EDT
Build Batch Data Pipelines on Google Cloud
Earned Jul 9, 2023 EDT
透過 Vertex AI 建構及部署機器學習解決方案
Earned Jul 4, 2023 EDT
在 Cloud Storage 建立安全的資料湖泊
Earned Jun 27, 2023 EDT
使用 Dataplex 整理及管理資料
Earned Jun 24, 2023 EDT
負責任的 AI 技術簡介
Earned Jun 12, 2023 EDT
編碼器-解碼器架構
Earned May 27, 2023 EDT
建立圖像說明生成模型
Earned May 27, 2023 EDT
圖像生成簡介
Earned May 27, 2023 EDT
Transformer 和 BERT 模型
Earned May 27, 2023 EDT
注意力機制
Earned May 27, 2023 EDT
大型語言模型簡介
Earned May 27, 2023 EDT
生成式 AI 簡介
Earned May 27, 2023 EDT
Google Cloud 必備知識
Earned Dec 2, 2021 EST
Learn how to use NotebookLM to create a personalized study guide for the Professional Machine Learning Engineer certification exam (PMLE). You'll review NotebookLM features, create a notebook, and use the study guide to practice for a certification exam.
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Gemini Enterprise 結合 Google 的搜尋和 AI 輔助功能，企業員工只要在單一搜尋列輸入關鍵字，就能查找文件儲存空間、電子郵件、對話、支援單處理系統和其他資料來源中的特定資訊。Gemini Enterprise 助理還能協助人員腦力激盪、研究資訊、列出文件大綱及執行其他動作，例如邀請同事加入日曆活動，加快完成知識型工作及各種協作作業。(請注意，Gemini Enterprise 先前稱為 Google Agentspace，本課程可能會提及產品舊稱。)
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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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This Data Analytics course consists of a series of advanced-level labs designed to validate your proficiency in using Google Cloud services. Each lab presents a set of the required tasks that you must complete with minimal assistance. The labs in this course have replaced the previous L300 Data Analytics Challenge Lab. If you have already completed the Challenge Lab as part of your L300 accreditation requirement, it will be carried over and count towards your L300 status. You must score 80% or higher for each lab to complete this course, and fulfill your CEPF L300 Data Analytics requirement. For technical issues with a Challenge Lab, please raise a Buganizer ticket using this CEPF Buganizer template: go/cepfl300labsupport
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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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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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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 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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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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「探索生成式 AI - Agent Platform」課程包含一系列實驗室， 介紹如何在 Google Cloud 使用生成式 AI。完成這些實驗室之後，您會瞭解如何 在 Agent Platform 使用 Gemini 模型、提示詞設計技巧與最佳做法，以及 AI 在構思想法和文字分類、擷取、摘要等方面的用途。您也會瞭解如何運用 Agent Platform 的自訂訓練功能調整基礎模型， 並部署至 Agent Platform 中的端點。
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This course helps learners create a study plan for the PDE (Professional Data Engineer) 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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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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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。
本課程將介紹擴散模型，這是一種機器學習模型，近期在圖像生成領域展現亮眼潛力。概念源自物理學，尤其深受熱力學影響。過去幾年來，在學術界和業界都是炙手可熱的焦點。在 Google Cloud 中，擴散模型是許多先進圖像生成模型和工具的基礎。課程將介紹擴散模型背後的理論，並說明如何在 Vertex AI 上訓練和部署這些模型。
Build a Certification Study Guide: PMLE
Earned Apr 29, 2025 EDT
運用 Gemini Enterprise 加速知識交流
Earned Apr 7, 2025 EDT
Feature Engineering
Earned Aug 6, 2024 EDT
Build, Train and Deploy ML Models with Keras on Google Cloud
Earned Jul 16, 2024 EDT
[CEPF L300 Course]: Data Analytics
Earned May 31, 2024 EDT
Launching into Machine Learning
Earned Apr 11, 2024 EDT
Google Cloud 的 AI 和機器學習服務簡介
Earned Jan 22, 2024 EST
Serverless Data Processing with Dataflow: Foundations
Earned Dec 18, 2023 EST
Smart Analytics, Machine Learning, and AI on Google Cloud
Earned Dec 16, 2023 EST
Build Streaming Data Pipelines on Google Cloud
Earned Dec 14, 2023 EST
Google Cloud Big Data and Machine Learning Fundamentals
Earned Sep 30, 2023 EDT
探索生成式 AI - Agent Platform
Earned Aug 3, 2023 EDT
Preparing for your Professional Data Engineer Journey
Earned Jul 12, 2023 EDT
Build Batch Data Pipelines on Google Cloud
Earned Jul 9, 2023 EDT
透過 Vertex AI 建構及部署機器學習解決方案
Earned Jul 4, 2023 EDT
在 Cloud Storage 建立安全的資料湖泊
Earned Jun 27, 2023 EDT
使用 Dataplex 整理及管理資料
Earned Jun 24, 2023 EDT
負責任的 AI 技術簡介
Earned Jun 12, 2023 EDT
編碼器-解碼器架構
Earned May 27, 2023 EDT
建立圖像說明生成模型
Earned May 27, 2023 EDT
圖像生成簡介
Earned May 27, 2023 EDT
Transformer 和 BERT 模型
Earned May 27, 2023 EDT
注意力機制
Earned May 27, 2023 EDT
大型語言模型簡介
Earned May 27, 2023 EDT
生成式 AI 簡介
Earned May 27, 2023 EDT
Google Cloud 必備知識
Earned Dec 2, 2021 EST