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Priyadharshini .K
Date d'abonnement : 2026
Ligue de Diamant
25925 points
Plan Your Cloud Next Generation Firewall Needs
Earned juil. 10, 2026 EDT
Introduction to Cloud Next Generation Firewall
Earned juil. 10, 2026 EDT
SecOps on GDC for Tier 3 Analysts
Earned juil. 10, 2026 EDT
SecOps on GDC for Tier 1 and Tier 2 Analysts
Earned juil. 10, 2026 EDT
Introduction to SecOps on GDC
Earned juil. 10, 2026 EDT
Google Security Operations - Deep Dive
Earned juil. 5, 2026 EDT
Model Armor: Securing AI Deployments
Earned juil. 3, 2026 EDT
Introduction to Security in the World of AI
Earned juil. 3, 2026 EDT
[Deprecated]Google Security Operations - Fundamentals
Earned juil. 3, 2026 EDT
Quiz: GDC Air-Gapped Security Operator Fundamentals
Earned juil. 3, 2026 EDT
Security Command Center Fundamentals
Earned juil. 3, 2026 EDT
Create Image Captioning Models
Earned juil. 1, 2026 EDT
Transformer Models and BERT Model
Earned juin 28, 2026 EDT
Encoder-Decoder Architecture
Earned juin 28, 2026 EDT
Attention Mechanism
Earned juin 28, 2026 EDT
Introduction to Image Generation
Earned juin 28, 2026 EDT
Responsible AI: Applying AI Principles with Google Cloud
Earned juin 28, 2026 EDT
[DEPRECATED]Introduction to Responsible AI
Earned juin 28, 2026 EDT
Machine Learning Operations (MLOps) for Generative AI
Earned juin 27, 2026 EDT
Introduction to Large Language Models
Earned juin 27, 2026 EDT
Responsible AI for Developers: Fairness & Bias
Earned juin 25, 2026 EDT
Introduction to Generative AI
Earned juin 24, 2026 EDT
Machine Learning Operations (MLOps) with Agent Platform: Model Evaluation
Earned juin 22, 2026 EDT
Machine Learning Operations (MLOps): Getting Started
Earned juin 22, 2026 EDT
Build a Certification Study Guide: PMLE
Earned juin 20, 2026 EDT
Introduction to Looker
Earned juin 14, 2026 EDT
Deploy and Scale AI Models with Cloud Run
Earned juin 12, 2026 EDT
Configure Gemini Code Assist for Organizations
Earned juin 12, 2026 EDT
[DEPRECATED] Gemini for end-to-end SDLC
Earned juin 12, 2026 EDT
Identify critical assets and their compliance requirements.
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This training course introduces Cloud NGFW. Topics include how Cloud NGFW provides centralized firewall management, centralized firewall visibility, advanced threat protection, and firewall insights.
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This course gives you a deep dive into the workflows of Tier 3 analysts.
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This course gives you a deep dive into the workflows of Tier 1 and Tier 2 security analysts.
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The first course provides a high-level overview of security fundamentals on the GDC platform.
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Take these next steps to learn more about the Google Security Operations Platform. Build on fundamental knowledge to go deeper on customization and tuning.
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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 apps. Explore other content in the Gemini Agent Ready (GEAR) program.
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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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This course covers the baseline skills needed for the Google Security Operations Platform. The modules will cover specific actions and features that security engineers should become familiar with to start using the toolset.
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This quiz tests your GDC Air-Gapped Security Operator Fundamental knowledge.
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Learn about the fundamental features of Security Command Center on Google Cloud. Spend time in this course to understand assets, detection and compliance. Security Command Center is a key part of your Google Cloud security journey, complete these modules and quiz to earn a completion badge.
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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 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 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 Gemini Enterprise Agent Platform.
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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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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 Gemini Enterprise Agent Platform empowers AI teams to streamline MLOps processes and achieve success in Generative AI projects.
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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. Explore other content in the Gemini Agent Ready (GEAR) program.
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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 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 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 Agent Platform, participants will learn how to implement robust evaluation processes for model selection, optimization, and continuous monitoring.
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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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Learn how to use Gemini Notebook to create a personalized study guide for the Professional Machine Learning Engineer (PMLE) certification exam. You'll review Gemini Notebook features, create a notebook, and use the study guide to practice for a certification exam.
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In this introductory course, you'll learn how Looker can help you explore, analyze, and visualize your data to drive better decisions. Through a combination of video lectures and demos, you'll discover how to connect to various data sources, build interactive dashboards, and perform effective data analysis. Whether you're a data analyst, BI analyst, data scientist or business user, this course will equip you with the foundational knowledge to start using Looker effectively, regardless of your background.
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AI inference is the process of using a trained machine learning model to make predictions on new, unseen data by applying learned patterns. This course is designed for developers, data scientists, and ML engineers interested in quickly deploying AI inference services on Cloud Run. It is useful for those familiar with cloud-based serverless application deployment solutions, but who may not have experience with running AI inference using Google Cloud serverless products.
The course includes examples that deploys a model for AI inference with GPUs and integrates gen AI apps with data storage services.
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This course is designed for Google Cloud developers and DevOps engineers who have basic knowledge of the Google Cloud console and are responsible for configuring Gemini Code Assist for an organization. The course introduces the benefits of Gemini Code Assist and compares the features of the different Gemini Code Assist editions. The course also shows you how to configure and manage Gemini Code Assist within an organization.
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In this course, you learn how Gemini, a generative AI-powered collaborator from Google Cloud, helps you use Google products and services to develop, test, deploy, and manage applications. With help from Gemini, you learn how to develop and build a web application, fix errors in the application, develop tests, and query data. Using a hands-on lab, you experience how Gemini improves the software development lifecycle (SDLC).
Duet AI was renamed to Gemini, our next-generation model.
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