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Vicente Wohl Valderrama

成为会员时间：2024

钻石联赛

33260 积分
Develop Advanced Enterprise Search and Conversation Applications Earned Feb 11, 2025 EST
注意力機制 Earned Feb 10, 2025 EST
Build and Deploy a Generative AI solution using a RAG framework Earned Jan 28, 2025 EST
Text Prompt Engineering Techniques Earned Oct 18, 2024 EDT
Integrate Vertex AI Search and Conversation into Voice and Chat Apps Earned Oct 16, 2024 EDT
負責任的 AI 技術：透過 Google Cloud 採用 AI 開發原則 Earned Oct 15, 2024 EDT
在 Google Cloud 打造生成式 AI 應用程式 Earned Oct 9, 2024 EDT
開發人員的負責任 AI 技術：公平性與偏誤 Earned Oct 3, 2024 EDT
Recommendation Systems on Google Cloud Earned Sep 4, 2024 EDT
透過 Vertex AI 建構及部署機器學習解決方案 Earned Sep 2, 2024 EDT
Computer Vision Fundamentals with Google Cloud Earned Aug 30, 2024 EDT
Production Machine Learning Systems Earned Aug 22, 2024 EDT
Feature Engineering Earned Aug 8, 2024 EDT
Build, Train and Deploy ML Models with Keras on Google Cloud Earned Aug 5, 2024 EDT
Launching into Machine Learning Earned Jul 30, 2024 EDT
Machine Learning Operations (MLOps): Getting Started Earned Mar 11, 2024 EDT
Machine Learning Operations (MLOps) with Vertex AI: Manage Features Earned Mar 11, 2024 EDT
Google Cloud 的 AI 和機器學習服務簡介 Earned Feb 20, 2024 EST

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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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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Demonstrate your ability to implement updated prompt engineering techniques and utilize several of Gemini's key capacilities including multimodal understanding and function calling. Then integrate generative AI into a RAG application deployed to Cloud Run. This course contains labs that are to be used as a test environment. They are deployed to test your understanding as a learner with a limited scope. These technologies can be used with fewer limitations in a real world environment.

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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 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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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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大型語言模型 (LLM) 誕生之後，生成式 AI 應用程式帶來的嶄新使用者體驗，可說是幾乎前所未有。身為應用程式開發人員，您要如何在 Google Cloud，運用生成式 AI 建立出色的互動式應用程式？ 本課程將帶您瞭解生成式 AI 應用程式，以及如何使用提示設計和檢索增強生成 (RAG)，透過 LLM 建構強大的應用程式。我們也會介紹可用於正式環境的生成式 AI 應用程式架構。您將建構採用 LLM 和 RAG 的對話應用程式。

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本課程旨在說明負責任 AI 技術的概念和 AI 開發原則，同時介紹各項技術，在實務上找出公平性和偏誤，減少 AI/機器學習做法上的偏誤。我們也將探討實用方法和工具，透過 Google Cloud 產品和開放原始碼工具，導入負責任 AI 技術的最佳做法。

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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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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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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 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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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 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 Vertex AI Feature Store's streaming ingestion at the SDK layer.

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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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