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Apply your skills in Google Cloud console

Earth Kumar Roy

Member since 2024

Google Cloud Computing Foundations: Cloud Computing Fundamentals Earned سبتمبر 29, 2025 EDT
Skills Boost Arcade Certification Zone September 2025 Earned سبتمبر 28, 2025 EDT
Level 2: AI and Data Innovation on Google Cloud Earned سبتمبر 27, 2025 EDT
Introduction to Security in the World of AI Earned سبتمبر 21, 2025 EDT
Responsible AI for Developers: Privacy & Safety Earned سبتمبر 21, 2025 EDT
Using BigQuery Machine Learning for Inference Earned سبتمبر 21, 2025 EDT
Responsible AI for Developers: Interpretability & Transparency Earned سبتمبر 20, 2025 EDT
Responsible AI for Developers: Fairness & Bias Earned سبتمبر 20, 2025 EDT
Introduction to Generative AI Earned سبتمبر 18, 2025 EDT
Google Sheets - Advanced Topics Earned سبتمبر 18, 2025 EDT
Machine Learning Operations (MLOps) with Vertex AI: Model Evaluation Earned مايو 7, 2025 EDT
Machine Learning Operations (MLOps) for Generative AI Earned مايو 7, 2025 EDT
Gemini for end-to-end SDLC Earned مايو 7, 2025 EDT
Gemini for Application Developers Earned مايو 7, 2025 EDT
Gemini for Data Scientists and Analysts Earned مايو 3, 2025 EDT

The Google Cloud Computing Foundations courses are for individuals with little to no background or experience in cloud computing. They provide an overview of concepts central to cloud basics, big data, and machine learning, and where and how Google Cloud fits in. By the end of the series of courses, learners will be able to articulate these concepts and demonstrate some hands-on skills. The courses should be completed in the following order: 1. Google Cloud Computing Foundations: Cloud Computing Fundamentals 2. Google Cloud Computing Foundations: Infrastructure in Google Cloud 3. Google Cloud Computing Foundations: Networking and Security in Google Cloud 4. Google Cloud Computing Foundations: Data, ML, and AI in Google Cloud This first course provides an overview of cloud computing, ways to use Google Cloud, and different compute options.

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Google Cloud Certifications provide a tangible way for you to demonstrate your skills to potential or current employers. These certifications incorporate performance-based questions, testing your hands-on expertise through practical tasks. Begin your journey towards becoming a Google Certified Professional with the help of the Arcade Cert Zone.

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Data complexity slows innovation, with studies showing that nearly 70% of organizations struggle to unify siloed data and apply AI effectively. This challenge takes you end-to-end—starting with solving data join pitfalls, building warehouses, and governing assets with Dataplex, then moving into automation with Document AI and hands-on generative AI. You’ll practice ingesting healthcare data, managing cloud storage, customizing AI models, and even building a chat app with the Gemini API—skills that reflect the real-world shift toward data-driven and AI-powered solutions.

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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 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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Learn about BigQuery ML for Inference, why Data Analysts should use it, its use cases, and supported ML models. You will also learn how to create and manage these ML models in BigQuery.

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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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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 builds on some of the concepts covered in the earlier Google Sheets course. In this course, you will learn how to apply and customize themes In Google Sheets, and explore conditional formatting options. You will learn about some of Google Sheets’ advanced formulas and functions. You will explore how to create formulas using functions, and you will also learn how to reference and validate your data in a Google Sheet. Spreadsheets can hold millions of numbers, formulas, and text. Making sense of all of that data can be difficult without a summary or visualization. This course explores the data visualization options in Google Sheets, such as charts and pivot tables. Google Forms are online surveys used to collect data and provide the opportunity for quick data analysis. You will explore how Forms and Sheets work together by connecting collected Form data to a spreadsheet, or by creating a Form from an existing spreadsheet.

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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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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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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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In this course, you learn how Gemini, a generative AI-powered collaborator from Google Cloud, helps analyze customer data and predict product sales. You also learn how to identify, categorize, and develop new customers using customer data in BigQuery. Using hands-on labs, you experience how Gemini improves data analysis and machine learning workflows. Duet AI was renamed to Gemini, our next-generation model.

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