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Vinarm Verma

Учасник із 2026

Срібна ліга

Кількість балів: 4187
Deploy and Scale AI Models with Cloud Run Earned жовт. 20, 2026 EDT
Configure Gemini Code Assist for Organizations Earned жовт. 20, 2026 EDT
[DEPRECATED] Gemini for end-to-end SDLC Earned жовт. 20, 2026 EDT
[DEPRECATED] Gemini for Application Developers Earned жовт. 20, 2026 EDT
Model Armor: Securing AI Deployments Earned жовт. 19, 2026 EDT
Introduction to Security in the World of AI Earned жовт. 19, 2026 EDT
Принципи відповідального використання ШІ для розробників: об’єктивність і упередженість Earned жовт. 19, 2026 EDT
Machine Learning Operations (MLOps) with Agent Platform: Model Evaluation Earned жовт. 19, 2026 EDT
Machine Learning Operations (MLOps) for Generative AI Earned жовт. 19, 2026 EDT

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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In this course, you learn how Gemini, a generative AI-powered collaborator from Google Cloud, helps developers build applications. You learn how to prompt Gemini to explain code, recommend Google Cloud services, and generate code for your applications. Using a hands-on lab, you experience how Gemini improves the application development workflow. Duet AI was renamed to Gemini, our next-generation model.

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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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Під час цього курсу ви зможете ознайомитися з концепціями відповідального підходу й принципами щодо штучного інтелекту. Ви дізнаєтеся про практичні методи виявлення об’єктивності й упередженості в роботі ШІ та технологій машинного навчання, а також ознайомитеся зі способами мінімізувати упередженість. У курсі розглядаються практичні методи й інструменти для впровадження відповідального підходу до ШІ за допомогою продуктів Google Cloud і інструментів із відкритим кодом.

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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 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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