HeyJaredGenerative AI Leader
Welcome to Google Generative AI Leader—an independent educational channel helping you prepare for the Google Cloud Generative AI Leader certification faster.
- Indexed videos, last 90 days
- 3
- Latest publication
- Aug 4, 2026
- Audience
- ~7 subscribers
- Earliest in this view
- Aug 4, 2026
Latest videos
Objective: 1.3 Identify the core layers of the gen AI landscape and the business implications. (opens the original)
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Core Layers of the Gen AI Landscape Navigate the generative AI landscape step-by-step so you can align infrastructure, models, and applications with your strategic business goals. In this tutorial, you will master the five core layers of the gen AI technology stack required for the exam. We break down Infrastructure, Models, Platforms, Agents, and Applications, explaining the specific business implications and value generated at every single layer. What you’ll learn: - Identify the five core lay
Objective 1.1 Describe core generative AI gen AI concepts and use cases. (opens the original)
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Master core generative AI concepts so you can identify powerful business use cases for your enterprise without getting lost in technical jargon. In this tutorial, you will master the foundational terminology required for the Google Cloud Generative AI Leader exam. We explore the machine learning lifecycle and explain the differences between foundation models, multimodal models, and diffusion models. You will also discover how to choose the appropriate foundation model based on modality, security
Objective: 1.2 Describe how various data types are used in gen AI and the business implications. (opens the original)
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Understand how AI data types impact business outcomes so you can ensure data quality and accessibility for your generative AI models. In this tutorial, we analyze the critical role data plays in generative AI success. You will learn the business implications of structured versus unstructured data, and how to evaluate data quality based on completeness, consistency, and cost. We also break down real-world enterprise examples of labeled and unlabeled data. What you’ll learn: Evaluate the character
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~7 subscribers
Measured Sep 19, 2026
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