Vertex AI is now Gemini Enterprise Agent Platform! We are currently updating our content to reflect this change. Please bear with us if you encounter naming inconsistencies during this transition.
Sarvesh Sarvesh
Member since 2024
Diamond League
64920 points
在 Compute Engine 導入 Cloud Load Balancing
Earned Jan 10, 2025 EST
Google Cloud Compute 基本操作
Earned Dec 31, 2024 EST
監控及管理 Google Cloud 資源
Earned Dec 31, 2024 EST
Google Cloud 監控工具
Earned Dec 31, 2024 EST
使用 Natural Language API 分析情緒
Earned Dec 30, 2024 EST
在 Cloud Storage 建立安全的資料湖泊
Earned Dec 30, 2024 EST
保護 BigLake 資料
Earned Dec 30, 2024 EST
實現 Cloud Storage 和數據保護解決方案
Earned Dec 30, 2024 EST
實現事件驅動的即時訊息與自動化工作流
Earned Dec 30, 2024 EST
Configure your Workplace: Google Workspace for IT Admins
Earned Dec 30, 2024 EST
實現雲端協作與生產力工作流
Earned Dec 29, 2024 EST
在 Google 試算表使用函式、公式及圖表
Earned Dec 29, 2024 EST
使用 Google API 分析語音和語言
Earned Dec 28, 2024 EST
在 BigQuery 執行預測資料分析
Earned Dec 28, 2024 EST
在 Google Cloud 使用 TensorFlow 分類圖像
Earned Dec 28, 2024 EST
使用 BigQuery ML 為預測模型進行資料工程
Earned Dec 27, 2024 EST
Exploring Vertex AI Search for Retail
Earned Dec 27, 2024 EST
Develop Advanced Enterprise Search and Conversation Applications
Earned Dec 27, 2024 EST
Build deterministic Virtual Agent enhanced with data stores
Earned Dec 26, 2024 EST
Build Streaming Data Pipelines on Google Cloud
Earned Nov 9, 2024 EST
Build and Deploy a Generative AI solution using a RAG framework
Earned Nov 6, 2024 EST
圖像生成簡介
Earned Nov 2, 2024 EDT
Vertex AI Studio 簡介
Earned Nov 2, 2024 EDT
Generative AI Fundamentals
Earned Nov 2, 2024 EDT
Text Prompt Engineering Techniques
Earned Nov 2, 2024 EDT
負責任的 AI 技術：透過 Google Cloud 採用 AI 開發原則
Earned Nov 1, 2024 EDT
Integrate Vertex AI Search and Conversation into Voice and Chat Apps
Earned Nov 1, 2024 EDT
建立圖像說明生成模型
Earned Oct 29, 2024 EDT
Transformer 和 BERT 模型
Earned Oct 29, 2024 EDT
編碼器-解碼器架構
Earned Oct 29, 2024 EDT
負責任的 AI 技術簡介
Earned Oct 29, 2024 EDT
注意力機制
Earned Oct 29, 2024 EDT
透過 BigQuery 建構資料倉儲
Earned Oct 8, 2024 EDT
Build Batch Data Pipelines on Google Cloud
Earned Oct 1, 2024 EDT
Production Machine Learning Systems
Earned Sep 6, 2024 EDT
透過 Vertex AI 建構及部署機器學習解決方案
Earned Sep 1, 2024 EDT
運用 BigQuery ML 建立機器學習模型
Earned Aug 28, 2024 EDT
Transform and Clean your Data with Dataprep by Alteryx on Google Cloud
Earned Aug 27, 2024 EDT
DEPRECATED Google Cloud Solutions II: Data and Machine Learning
Earned Aug 26, 2024 EDT
在 Google Cloud 為機器學習 API 準備資料
Earned Aug 25, 2024 EDT
Machine Learning in the Enterprise
Earned Aug 23, 2024 EDT
Feature Engineering
Earned Aug 19, 2024 EDT
Build, Train and Deploy ML Models with Keras on Google Cloud
Earned Aug 11, 2024 EDT
Build Data Lakes and Data Warehouses on Google Cloud
Earned Aug 10, 2024 EDT
Preparing for your Professional Data Engineer Journey
Earned Aug 1, 2024 EDT
Launching into Machine Learning
Earned Aug 1, 2024 EDT
Google Cloud 的 AI 和機器學習服務簡介
Earned Jul 20, 2024 EDT
Earn a skill badge by completing the Configure your Workplace: Google Workspace for IT Admins quest, where you will get try out the Admin role for Workspace and learn to provision Groups, manage applications, security, and manage Meet. A skill badge is an exclusive digital badge issued by Google Cloud in recognition of your proficiency with Google Cloud products and services and tests your ability to apply your knowledge in an interactive hands-on environment. Complete the skill badge quest, and final assessment challenge lab, to receive a digital badge that you can share with your network.
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完成「實現雲端協作與生產力工作流」課程，即可獲得入門級技能徽章。本課程將介紹 Google 的協作平台， 帶您瞭解如何使用 Gmail、日曆、Meet、雲端硬碟、試算表和 AppSheet。
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完成在 Google 試算表使用函式、公式及圖表技能徽章中階課程， 即可證明您具備下列技能：運用函式分析資料、使用圖表呈現資料、設定資料格式，以及搜尋、 驗證與顯示資料。
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完成「使用 Google API 分析語音和語言」課程， 瞭解如何將 Natural Language API 和 Speech API 投入實際應用， 即可獲得技能徽章。
完成「在 Google Cloud 使用 TensorFlow 分類圖像」技能徽章中階課程， 瞭解如何使用 TensorFlow 和 Vertex AI 建立及訓練機器學習模型， 即可獲得技能徽章。在 Vertex AI Workbench 中，你主要會和使用者自行管理的筆記本 互動。
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完成使用 BigQuery ML 為預測模型進行資料工程技能徽章中階課程， 即可證明自己具備下列知識與技能：運用 Dataprep by Trifacta 建構連至 BigQuery 的資料轉換 pipeline； 使用 Cloud Storage、Dataflow 和 BigQuery 建構「擷取、轉換及載入」(ETL) 工作負載， 以及使用 BigQuery ML 建構機器學習模型。
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This course provides hands-on experience with Google Cloud's Search for Retail, focusing on practical skills in setting up and managing retail search functionalities using APIs and console configurations. Participants will engage with real-world scenarios to learn how to import product data, manage user events, configure search parameters, and optimize search results within a retail environment.
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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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Demonstrate the ability to create and deploy deterministic virtual agents using Dialgflow CX and augment responses by grounding results on your own data integrating with Vertex AI Agent Builder data stores and leveraging Gemini for summarizations.
You will use the following technologies and Google Cloud services:
Vertex AI Agent Builder
Dialogflow CX
Gemini
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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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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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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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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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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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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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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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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.
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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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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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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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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Dataprep is Google's self-service data preparation tool built in collaboration with Alteryx. Learn the basics of cleaning and preparing data for analysis and visualization, all in the Google ecosystem. In this course, you will learn how to connect Dataprep to your data in Cloud Storage and BigQuery, clean data using the interactive UI, profile the data, and publish your results back into the Google ecosystem. You will learn the basics of data transformation, including filtering values, reshaping the data, combining multiple datasets, deriving new values, and aggregating your dataset.
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In this advanced-level quest, you will learn how to harness serious Google Cloud computing power to run big data and machine learning jobs. The hands-on labs will give you use cases, and you will be tasked with implementing big data and machine learning practices utilized by Google’s very own Solutions Architecture team. From running Big Query analytics on tens of thousands of basketball games, to training TensorFlow image classifiers, you will quickly see why Google Cloud is the go-to platform for running big data and machine learning jobs.
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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。
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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 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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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 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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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。
在 Compute Engine 導入 Cloud Load Balancing
Earned Jan 10, 2025 EST
Google Cloud Compute 基本操作
Earned Dec 31, 2024 EST
監控及管理 Google Cloud 資源
Earned Dec 31, 2024 EST
Google Cloud 監控工具
Earned Dec 31, 2024 EST
使用 Natural Language API 分析情緒
Earned Dec 30, 2024 EST
在 Cloud Storage 建立安全的資料湖泊
Earned Dec 30, 2024 EST
保護 BigLake 資料
Earned Dec 30, 2024 EST
實現 Cloud Storage 和數據保護解決方案
Earned Dec 30, 2024 EST
實現事件驅動的即時訊息與自動化工作流
Earned Dec 30, 2024 EST
Configure your Workplace: Google Workspace for IT Admins
Earned Dec 30, 2024 EST
實現雲端協作與生產力工作流
Earned Dec 29, 2024 EST
在 Google 試算表使用函式、公式及圖表
Earned Dec 29, 2024 EST
使用 Google API 分析語音和語言
Earned Dec 28, 2024 EST
在 BigQuery 執行預測資料分析
Earned Dec 28, 2024 EST
在 Google Cloud 使用 TensorFlow 分類圖像
Earned Dec 28, 2024 EST
使用 BigQuery ML 為預測模型進行資料工程
Earned Dec 27, 2024 EST
Exploring Vertex AI Search for Retail
Earned Dec 27, 2024 EST
Develop Advanced Enterprise Search and Conversation Applications
Earned Dec 27, 2024 EST
Build deterministic Virtual Agent enhanced with data stores
Earned Dec 26, 2024 EST
Build Streaming Data Pipelines on Google Cloud
Earned Nov 9, 2024 EST
Build and Deploy a Generative AI solution using a RAG framework
Earned Nov 6, 2024 EST
圖像生成簡介
Earned Nov 2, 2024 EDT
Vertex AI Studio 簡介
Earned Nov 2, 2024 EDT
Generative AI Fundamentals
Earned Nov 2, 2024 EDT
Text Prompt Engineering Techniques
Earned Nov 2, 2024 EDT
負責任的 AI 技術：透過 Google Cloud 採用 AI 開發原則
Earned Nov 1, 2024 EDT
Integrate Vertex AI Search and Conversation into Voice and Chat Apps
Earned Nov 1, 2024 EDT
建立圖像說明生成模型
Earned Oct 29, 2024 EDT
Transformer 和 BERT 模型
Earned Oct 29, 2024 EDT
編碼器-解碼器架構
Earned Oct 29, 2024 EDT
負責任的 AI 技術簡介
Earned Oct 29, 2024 EDT
注意力機制
Earned Oct 29, 2024 EDT
透過 BigQuery 建構資料倉儲
Earned Oct 8, 2024 EDT
Build Batch Data Pipelines on Google Cloud
Earned Oct 1, 2024 EDT
Production Machine Learning Systems
Earned Sep 6, 2024 EDT
透過 Vertex AI 建構及部署機器學習解決方案
Earned Sep 1, 2024 EDT
運用 BigQuery ML 建立機器學習模型
Earned Aug 28, 2024 EDT
Transform and Clean your Data with Dataprep by Alteryx on Google Cloud
Earned Aug 27, 2024 EDT
DEPRECATED Google Cloud Solutions II: Data and Machine Learning
Earned Aug 26, 2024 EDT
在 Google Cloud 為機器學習 API 準備資料
Earned Aug 25, 2024 EDT
Machine Learning in the Enterprise
Earned Aug 23, 2024 EDT
Feature Engineering
Earned Aug 19, 2024 EDT
Build, Train and Deploy ML Models with Keras on Google Cloud
Earned Aug 11, 2024 EDT
Build Data Lakes and Data Warehouses on Google Cloud
Earned Aug 10, 2024 EDT
Preparing for your Professional Data Engineer Journey
Earned Aug 1, 2024 EDT
Launching into Machine Learning
Earned Aug 1, 2024 EDT
Google Cloud 的 AI 和機器學習服務簡介
Earned Jul 20, 2024 EDT