Model Armor: Securing AI Deployments
Earned juin 6, 2026 EDT
Introduction to Security in the World of AI
Earned juin 6, 2026 EDT
Responsible AI for Developers: Fairness & Bias
Earned juin 5, 2026 EDT
Machine Learning Operations (MLOps) with Vertex AI: Model Evaluation
Earned juin 5, 2026 EDT
Machine Learning Operations (MLOps) for Generative AI
Earned juin 4, 2026 EDT
Introduction to Responsible AI
Earned juin 1, 2026 EDT
Introduction to Large Language Models
Earned juin 1, 2026 EDT
Introduction to Generative AI
Earned juin 1, 2026 EDT
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 applications.
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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 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 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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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.
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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.
Model Armor: Securing AI Deployments
Earned juin 6, 2026 EDT
Introduction to Security in the World of AI
Earned juin 6, 2026 EDT
Responsible AI for Developers: Fairness & Bias
Earned juin 5, 2026 EDT
Machine Learning Operations (MLOps) with Vertex AI: Model Evaluation
Earned juin 5, 2026 EDT
Machine Learning Operations (MLOps) for Generative AI
Earned juin 4, 2026 EDT
Introduction to Responsible AI
Earned juin 1, 2026 EDT
Introduction to Large Language Models
Earned juin 1, 2026 EDT
Introduction to Generative AI
Earned juin 1, 2026 EDT