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Fernanda Denisse Torrelio Torres (LATAM)

成为会员时间：2023

Google Cloud 資料分析簡介 Earned Dec 26, 2024 EST
Develop Advanced Enterprise Search and Conversation Applications Earned Oct 1, 2024 EDT
Integrate Vertex AI Search and Conversation into Voice and Chat Apps Earned Oct 1, 2024 EDT
Text Prompt Engineering Techniques Earned Sep 30, 2024 EDT
Machine Learning Operations (MLOps): Getting Started Earned Sep 25, 2024 EDT
Machine Learning in the Enterprise Earned Sep 25, 2024 EDT
Build, Train and Deploy ML Models with Keras on Google Cloud Earned Sep 25, 2024 EDT
Feature Engineering Earned Sep 24, 2024 EDT
Launching into Machine Learning Earned Sep 24, 2024 EDT
Google Cloud 的 AI 和機器學習服務簡介 Earned Sep 24, 2024 EDT
Vertex AI Studio 簡介 Earned Sep 23, 2024 EDT
圖像生成簡介 Earned Sep 23, 2024 EDT
負責任的 AI 技術：透過 Google Cloud 採用 AI 開發原則 Earned Sep 23, 2024 EDT
Generative AI Fundamentals Earned Sep 23, 2024 EDT
負責任的 AI 技術簡介 Earned Sep 23, 2024 EDT
大型語言模型簡介 Earned Sep 23, 2024 EDT
Serverless Data Processing with Dataflow: Foundations Earned May 22, 2024 EDT
Smart Analytics, Machine Learning, and AI on Google Cloud Earned May 21, 2024 EDT
Building Resilient Streaming Systems on Google Cloud Platform Earned May 16, 2024 EDT
Building Batch Data Pipelines on Google Cloud Earned Apr 29, 2024 EDT
Modernizing Data Lakes and Data Warehouses with Google Cloud Earned Apr 14, 2024 EDT
Preparing for your Professional Data Engineer Journey Earned Apr 13, 2024 EDT
Google Cloud Big Data and Machine Learning Fundamentals Earned Apr 13, 2024 EDT
Custom Search with Embeddings in Vertex AI Earned Apr 9, 2024 EDT
Vector Search 和嵌入 Earned Apr 4, 2024 EDT
Implementing Generative AI with Vertex AI Earned Apr 3, 2024 EDT
建立圖像說明生成模型 Earned Apr 2, 2024 EDT
Transformer 和 BERT 模型 Earned Apr 2, 2024 EDT
編碼器-解碼器架構 Earned Apr 2, 2024 EDT
注意力機制 Earned Apr 2, 2024 EDT
注意力機制 Earned Apr 2, 2024 EDT
生成式 AI 簡介 Earned Jan 27, 2024 EST

這堂初級課程將介紹 Google Cloud 的資料分析工作流程，以及用於探索、分析資料並以圖表呈現的工具。您也能學會如何與相關人員分享自己的發現結果。本課程包含個案研究、實作實驗室、講座、測驗和示範，實際展示如何將原始資料集轉化為清晰的資料，進而呈現出能發揮成效的圖表和資訊主頁。無論您是資料領域從業人員、想瞭解如何透過 Google Cloud 取得成功，或有意在職涯中更上一層樓，本課程都能協助您踏出第一步。絕大多數在工作上執行或運用資料分析的學員，都能從本課程受益。

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In this course, you'll use text embeddings for tasks like classification, outlier detection, text clustering and semantic search. You'll combine semantic search with the text generation capabilities of an LLM to build Retrieval Augmented Generation (RAG) solutions, such as for question-answering systems, using Google Cloud's Vertex AI and Google Cloud databases.

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This course on Integrate Vertex AI Search and Conversation into Voice and Chat Apps is composed of a set of labs to give you a hands on experience to interacting with new Generative AI technologies. You will learn how to create end-to-end search and conversational experiences by following examples. These technologies complement predefined intent-based chat experiences created in Dialogflow with LLM-based, generative answers that can be based on your own data. Also, they allow you to porvide enterprise-grade search experiences for internal and external websites to search documents, structure data and public websites.

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Text Prompt Engineering Techniques introduces you to consider different strategic approaches & techniques to deploy when writing prompts for text-based generative AI tasks.

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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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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 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 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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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 中的 AI 和機器學習 (ML) 服務。這些服務可建構預測式和生成式 AI 專案。我們將帶您探索「從資料到 AI」生命週期中適用的技術、產品和工具，包括 AI 基礎、開發選項及解決方案。課程目的是藉由生動的學習體驗與實作練習，增進數據資料學家、AI 開發人員和機器學習工程師的技能與知識。

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本課程會介紹 Vertex AI Studio。您可以運用這項工具和生成式 AI 模型互動、根據商業構想設計原型，並投入到正式環境。透過身歷其境的應用實例、有趣的課程及實作實驗室，您將能探索從提示到正式環境的生命週期，同時學習如何將 Vertex AI Studio 運用在多模態版 Gemini 應用程式、提示設計、提示工程和模型調整。這個課程的目標是讓您能運用 Vertex AI Studio，在專案中發揮生成式 AI 的潛能。

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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。

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隨著企業持續擴大使用人工智慧和機器學習，以負責任的方式發展相關技術也日益重要。對許多企業來說，談論負責任的 AI 技術可能不難，如何付諸實行才是真正的挑戰。如要瞭解如何在機構中導入負責任的 AI 技術，本課程絕對能助您一臂之力。 您可以從中瞭解 Google Cloud 目前採取的策略、最佳做法和經驗談，協助貴機構奠定良好基礎，實踐負責任的 AI 技術。

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Earn a skill badge by passing the final quiz, you'll demonstrate your understanding of foundational concepts in generative AI. A skill badge is a digital badge issued by Google Cloud in recognition of your knowledge of Google Cloud products and services. Share your skill badge by making your profile public and adding it to your social media profile.

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這個入門微學習課程主要介紹「負責任的 AI 技術」和其重要性，以及 Google 如何在自家產品中導入這項技術。本課程也會說明 Google 的 7 個 AI 開發原則。

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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。

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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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This 1-week, accelerated on-demand course builds upon Google Cloud Platform Big Data and Machine Learning Fundamentals. Through a combination of video lectures, demonstrations, and hands-on labs, you'll learn to build streaming data pipelines using Google cloud Pub/Sub and Dataflow to enable real-time decision making. You will also learn how to build dashboards to render tailored output for various stakeholder audiences.

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Data pipelines typically fall under one of the Extract and Load (EL), Extract, Load and Transform (ELT) or Extract, Transform and Load (ETL) paradigms. This course describes which paradigm should be used and when for batch data. Furthermore, this course covers several technologies on Google Cloud for data transformation including BigQuery, executing Spark on Dataproc, pipeline graphs in Cloud Data Fusion and serverless data processing with Dataflow. Learners get hands-on experience building data pipeline components on Google Cloud using Qwiklabs.

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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.

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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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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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This course explores Google Cloud technologies to create and generate embeddings. Embeddings are numerical representations of text, images, video and audio, and play a pivotal role in many tasks that involve the identification of similar items, like Google searches, online shopping recommendations, and personalized music suggestions. Specifically, you’ll use embeddings for tasks like classification, outlier detection, clustering and semantic search. You’ll combine semantic search with the text generation capabilities of an LLM to build Retrieval Augmented Generation (RAG) systems and question-answering solutions, on your own proprietary data using Google Cloud’s Vertex AI.

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這堂課程會介紹 AI 搜尋技術、工具和應用程式。主題涵蓋使用向量嵌入執行語意搜尋；結合語意和關鍵字做法的混合型搜尋機制；以及運用檢索增強生成 (RAG) 技術建構有基準的 AI 代理，盡可能減少 AI 幻覺。您可以實際使用 Vertex AI Vector Search，打造智慧型搜尋引擎。

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This course will help ML Engineers, Developers, and Data Scientists implement Large Language Models for Generative AI use cases with Vertex AI. The first two modules of this course contain links to videos and prerequisite course materials that will build your knowledge foundation in Generative AI. Please do not skip these modules. The advanced modules in this course assume you have completed these earlier modules.

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本課程說明如何使用深度學習來建立圖像說明生成模型。您將學習圖像說明生成模型的各個不同組成部分，例如編碼器和解碼器，以及如何訓練和評估模型。在本課程結束時，您將能建立自己的圖像說明生成模型，並使用模型產生圖像說明文字。

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這堂課程將說明變換器架構，以及基於變換器的雙向編碼器表示技術 (BERT) 模型，同時帶您瞭解變換器架構的主要組成 (如自我注意力機制) 和如何用架構建立 BERT 模型。此外，也會介紹 BERT 適用的各種任務，像是文字分類、問題回答和自然語言推論。課程預計約 45 分鐘。

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本課程概要說明解碼器與編碼器的架構，這種強大且常見的機器學習架構適用於序列對序列的任務，例如機器翻譯、文字摘要和回答問題。您將認識編碼器與解碼器架構的主要元件，並瞭解如何訓練及提供這些模型。在對應的研究室逐步操作說明中，您將學習如何從頭開始使用 TensorFlow 寫程式，導入簡單的編碼器與解碼器架構來產生詩詞。

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本課程將介紹注意力機制，說明這項強大技術如何讓類神經網路專注於輸入序列的特定部分。此外，也將解釋注意力的運作方式，以及如何使用注意力來提高各種機器學習任務的成效，包括機器翻譯、文字摘要和回答問題。

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本課程將介紹注意力機制，說明這項強大技術如何讓類神經網路專注於輸入序列的特定部分。此外，也將解釋注意力的運作方式，以及如何使用注意力來提高各種機器學習任務的成效，包括機器翻譯、文字摘要和回答問題。

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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。

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