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Sanjana Nagaraj

成为会员时间：2024

钻石联赛

32627 积分
Developing Applications with Cloud Run Functions on Google Cloud Earned Apr 29, 2026 EDT
在 Cloud Run 上开发无服务器应用 Earned Apr 24, 2026 EDT
Service Orchestration and Choreography on Google Cloud Earned Apr 10, 2026 EDT
图像生成简介 Earned Apr 10, 2026 EDT
大型语言模型简介 Earned Apr 10, 2026 EDT
生成式 AI 简介 Earned Apr 8, 2026 EDT
负责任的 AI 简介 Earned Apr 8, 2026 EDT
Migrate Microsoft SQL Server to Cloud SQL Earned Mar 31, 2026 EDT
Enterprise Database Migration - SQL Server Performance Analysis and Tuning with Cloud SQL Earned Jun 11, 2025 EDT
Modernize .Net Applications Earned Jun 11, 2025 EDT
Developing Applications with Google Cloud: Foundations Earned Jun 10, 2025 EDT
.NET Modernization Earned Jun 10, 2025 EDT
Launching into Machine Learning Earned Oct 10, 2024 EDT
Natural Language Processing on Google Cloud Earned Oct 10, 2024 EDT
在 Agent Platform 中设计提示 Earned Sep 20, 2024 EDT
Google Cloud 上的 AI 和机器学习简介 Earned Sep 20, 2024 EDT
App Deployment, Debugging, and Performance Earned Sep 5, 2024 EDT
Google Cloud: Prompt Engineering Guide Earned Sep 5, 2024 EDT
Google Cloud 基础知识：核心基础设施 Earned Sep 5, 2024 EDT

In this course, you learn about Cloud Run functions, Google's serverless, fully-managed functions as a service (FaaS) product that lets you implement single-purpose function code that reponds to HTTP requests and events from your cloud infrastructure.

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完成在 Cloud Run 上开发无服务器应用技能徽章中级课程， 展示您在以下方面的技能：集成 Cloud Run 与 Cloud Storage 以管理数据， 使用 Cloud Run 和 Pub/Sub 设计弹性异步系统架构， 构建依托 Cloud Run 技术的 REST API 网关，以及在 Cloud Run 上构建和部署服务。

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This course introduces you to event-based applications and teaches you how to use service orchestration and choreography to coordinate microservices. Using lectures and hands-on labs, you learn how to use Workflows, Eventarc, Cloud Tasks, and Cloud Scheduler to build microservices applications on Google Cloud.

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本课程向您介绍扩散模型。这类机器学习模型最近在图像生成领域展现出了巨大潜力。扩散模型的灵感来源于物理学，特别是热力学。过去几年内，扩散模型成为热门研究主题并在整个行业开始流行。Google Cloud 上许多先进的图像生成模型和工具都是以扩散模型为基础构建的。本课程向您介绍扩散模型背后的理论，以及如何在 Vertex AI 上训练和部署此类模型。

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这是一节入门级微学习课程，探讨什么是大型语言模型 (LLM)、适合的应用场景以及如何使用提示调整来提升 LLM 性能，还介绍了可以帮助您开发自己的 Gen AI 应用的各种 Google 工具。

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这是一节入门级微课程，旨在解释什么是生成式 AI、它的用途以及与传统机器学习方法的区别。该课程还介绍了可以帮助您开发自己的生成式 AI 应用的各种 Google 工具。

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这是一节入门级微课程，旨在解释什么是负责任的 AI、它的重要性，以及 Google 如何在自己的产品中实现负责任的 AI。此外，本课程还介绍了 Google 的 7 个 AI 开发原则。

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This skill badge aims to provide partners a comprehensive understanding of migrating Microsoft SQL Server databases to Cloud SQL for SQL Server, and gain hands-on experience through labs.

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Earn a DRI badge by completing the Enterprise Database Migration - SQL Server Performance Analysis and Tuning with Cloud SQL quest, where you demonstrate your capabilities of Cloud SQL Database Performance Monitoring for SQL Server, Cloud SQL Server Database Performance Analysis for SQL Server, and Cloud SQL Database Performance Tuning for SQL Server. When you complete this activity, you can earn the badge displayed above! View all the badges you have earned by visiting your profile page.

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This skill badge course is designed to offer hands-on experience through labs, enabling participants to migrate applications to the cloud using a "Rehost" strategy. Participants will learn essential tasks involved in migrating both frontend (.Net application) and backend (MySQL database) components to existing virtual machines. Through guided and challenge labs, participants will validate successful migrations, reinforcing their understanding of cloud application modernization concepts.

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In this course, you learn the fundamentals of application development on Google Cloud. You learn best practices for cloud applications, and how to select compute and data options to match your application use cases. You're introduced to generative AI and how it's used to help build applications. You learn about authentication and authorization, application deployment, continuous integration and delivery, and monitoring and performance tuning for your applications running in Google Cloud. Using lectures and hands-on labs, you learn how to get started building and running applications on Google Cloud.

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This learning path aims to upskill Google Cloud partners to perform the specific tasks associated with the priority workload. Learners will discover the specific tasks in rehosting applications from on-premises to Google Cloud. It also aims to re-platform applications to run in GKE. Learners will perform the tasks of Migrating MySQL, Angular, and .NET applications from their on-premises machines to Google Cloud VM instances. Sample code will be used during the migration. Learners will complete a challenge lab that focuses on the critical steps in a rehosting exercise - copying over code for the back-end, front-end, and middle-tier applications and validating that the applications have been migrated correctly. Learners will also complete a challenge lab that focuses on the critical steps in a re-platforming exercise - creating back-end, front-end, and middle-tier Docker images, deploying the same in the GKE cluster, and validating that the application has been deployed correctly.

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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 the products and solutions to solve NLP problems on Google Cloud. Additionally, it explores the processes, techniques, and tools to develop an NLP project with neural networks by using Vertex AI and TensorFlow.

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完成 在 Agent Platform 中设计提示入门技能徽章课程，展示以下方面的技能： Agent Platform 中的提示工程、图片分析和多模态生成式技术。探索如何编写有效的提示，指导生成式 AI 输出， 以及将 Gemini 模型应用于真实的营销场景。

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本课程介绍 Google Cloud 的 AI 和机器学习 (ML) 能力，重点讲解如何开发生成式和预测式 AI 项目。本课程将探讨“数据到 AI”全生命周期中的多种技术、产品和工具，并通过互动练习帮助数据科学家、AI 开发者和机器学习工程师提升专业能力。

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In this course, application developers learn how to design and develop cloud-native applications that seamlessly integrate components from the Google Cloud ecosystem. Through a combination of presentations, demos, and hands-on labs, participants learn how to create repeatable deployments by treating infrastructure as code, choose the appropriate application execution environment for an application, and monitor application performance. Completing one version of each lab is required. Each lab is available in Node.js. In most cases, the same labs are also provided in Python or Java. You may complete each lab in whichever language you prefer.

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Google Cloud : Prompt Engineering Guide examines generative AI tools, how they work. We'll explore how to combine Google Cloud knowledge with prompt engineering to improve Gemini responses.

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“Google Cloud 基础知识：核心基础设施”介绍在使用 Google Cloud 时会遇到的重要概念和术语。本课程通过视频和实操实验来介绍并比较 Google Cloud 的多种计算和存储服务，并提供重要的资源和政策管理工具。

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