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Date d'abonnement : 2025

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62614 points
Responsible AI for Developers: Privacy & Safety Earned juin 8, 2025 EDT
Inspect Rich Documents with Gemini Multimodality and Multimodal RAG Earned juin 8, 2025 EDT
Responsible AI for Developers: Fairness & Bias Earned juin 8, 2025 EDT
Responsible AI for Developers: Interpretability & Transparency Earned juin 8, 2025 EDT
Vector Search and Embeddings Earned juin 8, 2025 EDT
Responsible AI: Applying AI Principles with Google Cloud Earned juin 8, 2025 EDT
Introduction to Responsible AI Earned juin 8, 2025 EDT
Automate Data Capture at Scale with Document AI Earned juin 8, 2025 EDT
Machine Learning Operations (MLOps) with Vertex AI: Model Evaluation Earned juin 7, 2025 EDT
Prompt Design in Vertex AI Earned juin 7, 2025 EDT
Get Started with Dataplex Earned juin 5, 2025 EDT
Store, Process, and Manage Data on Google Cloud - Console Earned juin 4, 2025 EDT
Introduction to Vertex AI Studio Earned juin 4, 2025 EDT
Generative AI Explorer - Vertex AI Earned juin 4, 2025 EDT
Multi-Cluster, Multi-Cloud with Anthos Earned juin 4, 2025 EDT
Essential Google Cloud CLI Tools Earned juin 3, 2025 EDT
Manage Kubernetes in Google Cloud Earned juin 3, 2025 EDT
Classify Images with TensorFlow on Google Cloud Earned juin 3, 2025 EDT
Networking in Google Cloud: Network Architecture Earned juin 3, 2025 EDT
BigQuery for Machine Learning Earned juin 2, 2025 EDT
Create and Manage Cloud Spanner Instances Earned juin 2, 2025 EDT
Build Infrastructure with Terraform on Google Cloud Earned juin 2, 2025 EDT
Implement DevOps Workflows in Google Cloud Earned juin 2, 2025 EDT
Build Real World AI Applications with Gemini and Imagen Earned juin 1, 2025 EDT
Enrich Metadata and Discovery of BigLake Data Earned juin 1, 2025 EDT
Introduction to Security in the World of AI Earned mai 29, 2025 EDT
Gemini for Security Engineers Earned mai 27, 2025 EDT
Encoder-Decoder Architecture Earned mai 27, 2025 EDT
Attention Mechanism Earned mai 27, 2025 EDT
Introduction to Image Generation Earned mai 27, 2025 EDT
Introduction to Large Language Models Earned mai 27, 2025 EDT
Introduction to Generative AI Earned mai 27, 2025 EDT
Transformer Models and BERT Model Earned mai 27, 2025 EDT
Create Image Captioning Models Earned mai 27, 2025 EDT
Machine Learning Operations (MLOps) for Generative AI Earned mai 27, 2025 EDT

This course introduces important topics of AI privacy and safety. It explores practical methods and tools to implement AI privacy and safety recommended practices through the use of Google Cloud products and open-source tools.

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Complete the intermediate Inspect Rich Documents with Gemini Multimodality and Multimodal RAG skill badge course to demonstrate skills in the following: using multimodal prompts to extract information from text and visual data, generating a video description, and retrieving extra information beyond the video using multimodality with Gemini; building metadata of documents containing text and images, getting all relevant text chunks, and printing citations by using Multimodal Retrieval Augmented Generation (RAG) with Gemini.

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This course introduces concepts of responsible AI and AI principles. It covers techniques to practically identify fairness and bias and mitigate bias in AI/ML practices. It explores practical methods and tools to implement Responsible AI best practices using Google Cloud products and open source tools.

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This course introduces concepts of AI interpretability and transparency. It discusses the importance of AI transparency for developers and engineers. It explores practical methods and tools to help achieve interpretability and transparency in both data and AI models.

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Explore AI-powered search technologies, tools, and applications in this course. Learn semantic search utilizing vector embeddings, hybrid search combining semantic and keyword approaches, and retrieval-augmented generation (RAG) minimizing AI hallucinations as a grounded AI agent. Gain practical experience with Vertex AI Vector Search to build your intelligent search engine.

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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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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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Earn the introductory skill badge by completing the Automate Data Capture at Scale with Document AI course. In this course, you learn how to extract, process, and capture data using Document AI.

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This course equips machine learning practitioners with the essential tools, techniques, and best practices for evaluating both generative and predictive AI models. Model evaluation is a critical discipline for ensuring that ML systems deliver reliable, accurate, and high-performing results in production. Participants will gain a deep understanding of various evaluation metrics, methodologies, and their appropriate application across different model types and tasks. The course will emphasize the unique challenges posed by generative AI models and provide strategies for tackling them effectively. By leveraging Google Cloud's Vertex AI platform, participants will learn how to implement robust evaluation processes for model selection, optimization, and continuous monitoring.

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Complete the introductory Prompt Design in Vertex AI skill badge to demonstrate skills in the following: prompt engineering, image analysis, and multimodal generative techniques, within Vertex AI. Discover how to craft effective prompts, guide generative AI output, and apply Gemini models to real-world marketing scenarios.

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Complete the introductory Get Started with Dataplex skill badge to demonstrate skills in the following: creating Dataplex assets, creating aspect types, and applying aspects to entries in Dataplex.

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Cloud Storage, Cloud Functions, and Cloud Pub/Sub are all Google Cloud Platform services that can be used to store, process, and manage data. All three services can be used together to create a variety of data-driven applications. In this skill badge you use Cloud Storage to store images, Cloud Functions to process the images, and Cloud Pub/Sub to send the images to another application.

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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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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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Course one of the Architecting Hybrid Cloud with Anthos series introduces participants to manage multi-cloud and hybrid Kubernetes deployments using Anthos. Through presentations and hands-on labs, participants explore planning and creating Anthos environments and building manageable and reliable multi-cluster Kubernetes infrastructure environments centered around Anthos and containers. This course is a continuation of Architecting with GKE and assumes direct experience with the technologies covered in that course.

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Master Google Cloud from your terminal! In this course you will rapidly learn essential gcloud command-line skills through short hands-on labs. Gain practical experience managing Compute Engine, Kubernetes Engine, networking, and BigQuery with gcloud; plus, efficient Cloud Storage management with gsutil. You'll finish the entire course in 75 minutes!

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Complete the intermediate Manage Kubernetes in Google Cloud skill badge course to demonstrate skills in the following: managing deployments with kubectl, monitoring and debugging applications on Google Kubernetes Engine (GKE), and continuous delivery techniques.

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Earn the intermediate Skill Badge by completing the Classify Images with TensorFlow on Google Cloud skill badge course where you learn how to use TensorFlow and Vertex AI to create and train machine learning models. You primarily interact with Vertex AI Workbench user-managed notebooks.

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Welcome to the third course of the "Networking in Google Cloud" series: Network Architecture! In this course, you will explore the fundamentals of designing efficient and scalable network architectures within Google Cloud. In the first module, Introduction to Network Architecture, we'll start by introducing you to the core components and concepts of network architecture, including subnets, routes, firewalls, and load balancing. Then in the second module, network topologies, we'll dive into various network topologies commonly used in Google Cloud, discussing their strengths, and weaknesses.

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Want to build ML models in minutes instead of hours using just SQL? BigQuery ML democratizes machine learning by letting data analysts create, train, evaluate, and predict with machine learning models using existing SQL tools and skills. In this series of labs, you will experiment with different model types and learn what makes a good model.

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Complete the introductory Create and Manage Cloud Spanner Instances skill badge to demonstrate skills in the following: creating and interacting with Cloud Spanner instances and databases; loading Cloud Spanner databases using various techniques; backing up Cloud Spanner databases; defining schemas and understanding query plans; and deploying a Modern Web App connected to a Cloud Spanner instance.

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Complete the intermediate Build Infrastructure with Terraform on Google Cloud skill badge to demonstrate skills in the following: Infrastructure as Code (IaC) principles using Terraform, provisioning and managing Google Cloud resources with Terraform configurations, effective state management (local and remote), and modularizing Terraform code for reusability and organization.

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Complete the intermediate Implement DevOps Workflows in Google Cloud skill badge to demonstrate skills in the following: creating git repositories with Cloud Source Repositories, launching, managing, and scaling deployments on Google Kubernetes Engine (GKE), and architecting CI/CD pipelines that automate container image builds and deployments to GKE.

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Complete the introductory Build Real World AI Applications with Gemini and Imagen skill badge to demonstrate skills in the following: image recognition, natural language processing, image generation using Google's powerful Gemini and Imagen models, deploying applications on the Vertex AI platform.

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Complete the Enrich Metadata and Discovery of BigLake Data skill badge course to demonstrate skills in BigQuery, BigLake, and Dataplex Universal Catalog. You create BigLake tables and enrich metadata management and discovery of the table data.

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Artificial Intelligence (AI) offers transformative possibilities, but it also introduces new security challenges. This course equips security and data protection leaders with strategies to securely manage AI within their organizations. Learn a framework for proactively identifying and mitigating AI-specific risks, protecting sensitive data, ensuring compliance, and building a resilient AI infrastructure. Pick use cases from four different industries to explore how these strategies apply in real-world scenarios.

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In this course, you learn how Gemini, a generative AI-powered collaborator from Google Cloud, helps you secure your cloud environment and resources. You learn how to deploy example workloads into an environment in Google Cloud, identify security misconfigurations with Gemini, and remediate security misconfigurations with Gemini. Using a hands-on lab, you experience how Gemini improves your cloud security posture. Duet AI was renamed to Gemini, our next-generation model.

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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 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 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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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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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 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 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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