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Armin Sarjou
Date d'abonnement : 2024
Data Transformation in the Cloud
Earned déc. 2, 2025 EST
The Power of Storytelling: How to Visualize Data in the Cloud
Earned nov. 20, 2025 EST
Data Management and Storage in the Cloud
Earned oct. 28, 2025 EDT
Introduction to Data Analytics in Google Cloud
Earned oct. 27, 2025 EDT
API Design and Fundamentals with the Apigee API Platform
Earned juil. 10, 2025 EDT
Google Cloud Fundamentals: Core Infrastructure
Earned juil. 1, 2025 EDT
Introduction to Security in the World of AI
Earned juin 24, 2025 EDT
Responsible AI for Developers: Privacy & Safety
Earned juin 24, 2025 EDT
Responsible AI for Developers: Interpretability & Transparency
Earned juin 24, 2025 EDT
Responsible AI for Developers: Fairness & Bias
Earned juin 24, 2025 EDT
Machine Learning Operations (MLOps) with Agent Platform: Model Evaluation
Earned juin 24, 2025 EDT
Machine Learning Operations (MLOps) for Generative AI
Earned juin 24, 2025 EDT
The Power of Storytelling: How to Visualize Data in the Cloud
Earned juin 23, 2025 EDT
Responsible AI: Applying AI Principles with Google Cloud
Earned juin 19, 2025 EDT
Prompt Design in Agent Platform
Earned juin 19, 2025 EDT
Data Transformation in the Cloud
Earned avr. 10, 2025 EDT
Data Management and Storage in the Cloud
Earned mars 10, 2025 EDT
[DEPRECATED]Introduction to Responsible AI
Earned mai 6, 2024 EDT
Introduction to Large Language Models
Earned mai 6, 2024 EDT
Introduction to Generative AI
Earned mai 6, 2024 EDT
Introduction to Data Analytics in Google Cloud
Earned mai 6, 2024 EDT
This is the third of five courses in the Google Cloud Data Analytics Certificate. In this course, you’ll begin by getting an overview of the data journey, from collection to insights. You’ll then learn how to use SQL to transform raw data into a usable format. Next, you’ll learn how to transform high volumes of data with a data pipeline. Finally, you’ll gain experience applying transformation strategies to real data sets to solve business needs.
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You’re over halfway through the program! This is the fourth course of the Google Cloud Data Analytics Certificate. Level up your data visualization skills by unlocking the secrets of transforming flat data into captivating stories that leap from the screen and onto the agenda of your key stakeholders. Happy learning!
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Hi again! This is the second course of the Google Cloud Data Analytics Certificate. Get cozy with the key components of data governance, normalized and star schemas, data catalogs, and data lakehouse architecture.
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Welcome learner! This is the first of five courses of the Google Cloud Data Analytics Certificate. Jump into the world of data analytics and cloud computing. Explore the data lifecycle in the cloud and gain a better understanding of the common tools used by data analysts.
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In this course, you learn how to design APIs, and how to use OpenAPI specifications to document them. You learn about the API lifecycle, and how the Apigee API platform helps you manage all aspects of the lifecycle. You learn how APIs can be designed using API proxies, and how APIs are packaged as API products to be used by app developers.
Through a combination of lessons, hands-on labs, and supplemental materials, you will learn how to design, build, secure, deploy, and manage API solutions using Google Cloud's Apigee API Platform. This is the first course of the Developing APIs with the Apigee API Platform series. After completing this course, enroll in the API Security with the Apigee API Platform course.
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Google Cloud Fundamentals: Core Infrastructure introduces important concepts and terminology for working with Google Cloud. Through videos and hands-on labs, this course presents and compares many of Google Cloud's computing and storage services, along with important resource and policy management tools.
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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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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 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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This is the fourth of five courses in the Google Cloud Data Analytics Certificate. In this course, you’ll focus on developing skills in the five key stages of visualizing data in the cloud: storytelling, planning, exploring data, building visualizations, and sharing data with others. You’ll also gain experience using UI/UX skills to wireframe impactful, cloud-native visualizations and work with cloud-native data visualization tools to explore datasets, create reports, and build dashboards that drive decisions and foster collaboration.
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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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Complete the introductory Prompt Design in Agent Platform skill badge to demonstrate skills in the following: prompt engineering, image analysis, and multimodal generative techniques, within Agent Platform. Discover how to craft effective prompts, guide generative AI output, and apply Gemini models to real-world marketing scenarios. Explore other content in the Gemini Enterprise Agent Ready (GEAR) program.
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This is the third of five courses in the Google Cloud Data Analytics Certificate. In this course, you’ll begin by getting an overview of the data journey, from collection to insights. You’ll then learn how to use SQL to transform raw data into a usable format. Next, you’ll learn how to transform high volumes of data with a data pipeline. Finally, you’ll gain experience applying transformation strategies to real data sets to solve business needs.
En savoir plus
This is the second of five courses in the Google Cloud Data Analytics Certificate. In this course, you’ll explore how data is structured and organized. You’ll gain hands-on experience with the data lakehouse architecture and cloud components like BigQuery, Google Cloud Storage, and DataProc to efficiently store, analyze, and process large datasets.
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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 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 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 is the first of five courses in the Google Cloud Data Analytics Certificate. In this course, you’ll define the field of cloud data analysis and describe roles and responsibilities of a cloud data analyst as they relate to data acquisition, storage, processing, and visualization. You’ll explore the architecture of Google Cloud-based tools, like BigQuery and Cloud Storage, and how they are used to effectively structure, present, and report data.
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