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Mettez en pratique vos compétences dans la console Google Cloud

David Zhu

Date d'abonnement : 2022

Ligue de Diamant

16293 points
Model Armor: Securing AI Deployments Earned sept. 16, 2025 EDT
Explore Google's Gen AI Models Earned sept. 9, 2025 EDT
Boost Productivity with Gemini in BigQuery Earned sept. 9, 2025 EDT
Generative AI Fundamentals Earned sept. 8, 2025 EDT
Generative AI for Business Leaders Earned sept. 8, 2025 EDT
Gen AI Agents: Transform Your Organization Earned juin 20, 2025 EDT
Gen AI Apps: Transform Your Work Earned juin 20, 2025 EDT
Gen AI: Navigate the Landscape Earned juin 20, 2025 EDT
Gen AI: Unlock Foundational Concepts Earned juin 19, 2025 EDT
Gen AI: Beyond the Chatbot Earned juin 19, 2025 EDT
L300 Google Distributed Cloud connected Earned avr. 25, 2025 EDT
L200 Google Distributed Cloud Connected Earned avr. 24, 2025 EDT
Introduction to Google Distributed Cloud connected Earned avr. 23, 2025 EDT
Machine Learning Operations (MLOps) for Generative AI Earned mars 25, 2025 EDT
Accelerate Knowledge Exchange with Gemini Enterprise Earned jan. 29, 2025 EST
Deploying Search and Chat Apps using Agent Builder Earned sept. 27, 2024 EDT
Responsible AI: Applying AI Principles with Google Cloud Earned fév. 14, 2024 EST
Rapid Migration & Modernization Program Earned jan. 3, 2024 EST
Text Prompt Engineering Techniques Earned juin 20, 2023 EDT
Implementing Generative AI with Vertex AI Earned juin 19, 2023 EDT
Generative AI Explorer - Vertex AI Earned juin 14, 2023 EDT
Introduction to Vertex AI Studio Earned juin 14, 2023 EDT
Create Image Captioning Models Earned juin 13, 2023 EDT
Encoder-Decoder Architecture Earned juin 13, 2023 EDT
Introduction to Image Generation Earned juin 8, 2023 EDT
Generative AI Fundamentals Earned juin 7, 2023 EDT
Introduction to Responsible AI Earned juin 7, 2023 EDT
Transformer Models and BERT Model Earned juin 5, 2023 EDT
Attention Mechanism Earned juin 5, 2023 EDT
Introduction to Large Language Models Earned mai 16, 2023 EDT
Introduction to Generative AI Earned mai 15, 2023 EDT

This course reviews the essential security features of Model Armor and equips you to work with the service. You’ll learn about the security risks associated with LLMs and how Model Armor protects your AI applications.

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Learn how to leverage Gemini multimodal capabilities to process and generate text, images, and audio and to integrate Gemini through APIs to perform tasks such as content creation and summarization.

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This course explores Gemini in BigQuery, a suite of AI-driven features to assist data-to-AI workflow. These features include data exploration and preparation, code generation and troubleshooting, and workflow discovery and visualization. Through conceptual explanations, a practical use case, and hands-on labs, the course empowers data practitioners to boost their productivity and expedite the development pipeline.

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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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A Business Leader in Generative AI can articulate the capabilities of core cloud Generative AI products and services and understand how they benefit organizations. This course provides an overview of the types of opportunities and challenges that companies often encounter in their digital transformation journey and how they can leverage Google Cloud's generative AI products to overcome these challenges.

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Gen AI Agents: Transform Your Organization is the fifth and final course of the Gen AI Leader learning path. This course explores how organizations can use custom gen AI agents to help tackle specific business challenges. You gain hands-on practice building a basic gen AI agent, while exploring the components of these agents, such as models, reasoning loops, and tools.

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Transform Your Work With Gen AI Apps is the fourth course of the Gen AI Leader learning path. This course introduces Google’s gen AI applications, such as Google Workspace with Gemini and NotebookLM. It guides you through concepts like grounding, retrieval augmented generation, constructing effective prompts and building automated workflows.

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Gen AI: Navigate the Landscape is the third course of the Gen AI Leader learning path. Gen AI is changing how we work and interact with the world around us. But as a leader, how can you harness its power to drive real business outcomes? In this course, you explore the different layers of building gen AI solutions, Google Cloud’s offerings, and the factors to consider when selecting a solution.

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Gen AI: Unlock Foundational Concepts is the second course of the Gen AI Leader learning path. In this course, you unlock the foundational concepts of generative AI by exploring the differences between AI, ML, and gen AI, and understanding how various data types enable generative AI to address business challenges. You also gain insights into Google Cloud strategies to address the limitations of foundation models and the key challenges for responsible and secure AI development and deployment.

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Gen AI: Beyond the Chatbot is the first course of the Gen AI Leader learning path and has no prerequisites. This course aims to move beyond the basic understanding of chatbots to explore the true potential of generative AI for your organization. You explore concepts like foundation models and prompt engineering, which are crucial for leveraging the power of gen AI. The course also guides you through important considerations you should make when developing a successful gen AI strategy for your organization.

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This L300 course offers a comprehensive exploration of GDC connected, encompassing design, deployment, operations, and advanced networking. It explores the architecture, configuration, and management of GDC infrastructure, including server setup, control plane operations, and network connectivity. The course also covers operational aspects, like high availability, failover, and observability, along with advanced networking topics such as APIs, network functions, and plugins.

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This L200 course comprehensively explores GDC connected concepts, architecture, and operational aspects, equipping learners with the knowledge to deploy and manage this solution effectively. The course delves into topics such as its survivability features and best practices, security, networking, software stack, and hardware options. Furthermore, the course provides insights into its operating model. By the end of this course, learners will have a solid understanding of GDC connected and be prepared to leverage its capabilities for their organization's needs.

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In this course, you will learn about GDC connected (previously known as GDC Edge), an offering from Google Distributed Cloud. This course provides both a business and technical overview of GDC connected, exploring its key features and target customers. Participants will gain insights into GDC connected's value proposition and learn how to effectively communicate its benefits to potential clients, enabling them to qualify for sales opportunities.

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This course is dedicated to equipping you with the knowledge and tools needed to uncover the unique challenges faced by MLOps teams when deploying and managing Generative AI models, and exploring how Vertex AI empowers AI teams to streamline MLOps processes and achieve success in Generative AI projects.

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Unite Google’s expertise in search and AI with Gemini Enterprise, a powerful tool designed to help employees find specific information from document storage, email, chats, ticketing systems, and other data sources, all from a single search bar. The Gemini Enterprise assistant can also help brainstorm, research, outline documents, and take actions like inviting coworkers to a calendar event to accelerate knowledge work and collaboration of all kinds. (Please note Gemini Enterprise was previously named Google Agentspace, there may be references to the previous product name in this course.)

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This quest will test your ability to create and deploy both Search applications and Chatbots using Vertex AI Agent Builder and Dialgflow. You will also be tasked with implementing a custom RAG system that uses the Discovery API to query a Vertex AI Data Store and use Gemini to answer user questions. You will use the following technologies and Google Cloud services: Vertex AI Agent Builder Search Apps Agents Gemini The assessment is divided into three main tasks: Building and deploying a Website Search App Deploying a Chatbot built using Vertex AI Agent Builder Creating a Custom Q&A Solution using the Discovery API

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As the use of enterprise Artificial Intelligence and Machine Learning continues to grow, so too does the importance of building it responsibly. A challenge for many is that talking about responsible AI can be easier than putting it into practice. If you’re interested in learning how to operationalize responsible AI in your organization, this course is for you. In this course, you will learn how Google Cloud does this today, together with best practices and lessons learned, to serve as a framework for you to build your own responsible AI approach.

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The Google Cloud Rapid Migration & Modernization Program (RaMP) is a holistic, end-to-end migration/modernization program that helps customers & partners leverage expertise and best practices, lower risk, control costs, and simplify a customer's path to cloud success. This course will give an overview of the program and some of the tools and best practices available to support customer migrations & modernizations.

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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 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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The Generative AI Explorer - Vertex Quest is a collection of labs on how to use Generative AI on Google Cloud. Through the labs, you will learn about how to use the models in the Vertex AI PaLM API family, including text-bison, chat-bison, and textembedding-gecko. You will also learn about prompt design, best practices, and how it can be used for ideation, text classification, text extraction, text summarization, and more. You will also learn how to tune a foundation model by training it via Vertex AI custom training and deploy it to a Vertex AI endpoint.

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This course introduces Vertex AI Studio, a tool to interact with generative AI models, prototype business ideas, and launch them into production. Through an immersive use case, engaging lessons, and a hands-on lab, you’ll explore the prompt-to-product lifecycle and learn how to leverage Vertex AI Studio for Gemini multimodal applications, prompt design, prompt engineering, and model tuning. The aim is to enable you to unlock the potential of gen AI in your projects with Vertex AI Studio.

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This course teaches you how to create an image captioning model by using deep learning. You learn about the different components of an image captioning model, such as the encoder and decoder, and how to train and evaluate your model. By the end of this course, you will be able to create your own image captioning models and use them to generate captions for images

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This course gives you a synopsis of the encoder-decoder architecture, which is a powerful and prevalent machine learning architecture for sequence-to-sequence tasks such as machine translation, text summarization, and question answering. You learn about the main components of the encoder-decoder architecture and how to train and serve these models. In the corresponding lab walkthrough, you’ll code in TensorFlow a simple implementation of the encoder-decoder architecture for poetry generation from the beginning.

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This course introduces diffusion models, a family of machine learning models that recently showed promise in the image generation space. Diffusion models draw inspiration from physics, specifically thermodynamics. Within the last few years, diffusion models became popular in both research and industry. Diffusion models underpin many state-of-the-art image generation models and tools on Google Cloud. This course introduces you to the theory behind diffusion models and how to train and deploy them on Vertex AI.

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Earn a skill badge by completing the Introduction to Generative AI, Introduction to Large Language Models and Introduction to Responsible AI courses. 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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This is an introductory-level microlearning course aimed at explaining what responsible AI is, why it's important, and how Google implements responsible AI in their products. It also introduces Google's 3 AI principles.

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This course introduces you to the Transformer architecture and the Bidirectional Encoder Representations from Transformers (BERT) model. You learn about the main components of the Transformer architecture, such as the self-attention mechanism, and how it is used to build the BERT model. You also learn about the different tasks that BERT can be used for, such as text classification, question answering, and natural language inference.This course is estimated to take approximately 45 minutes to complete.

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This course will introduce you to the attention mechanism, a powerful technique that allows neural networks to focus on specific parts of an input sequence. You will learn how attention works, and how it can be used to improve the performance of a variety of machine learning tasks, including machine translation, text summarization, and question answering. This course is estimated to take approximately 45 minutes to complete.

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This is an introductory level micro-learning course that explores what large language models (LLM) are, the use cases where they can be utilized, and how you can use prompt tuning to enhance LLM performance. It also covers Google tools to help you develop your own Gen AI apps.

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This is an introductory level microlearning course aimed at explaining what Generative AI is, how it is used, and how it differs from traditional machine learning methods. It also covers Google Tools to help you develop your own Gen AI apps.

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