Anthony Benavides
Member since 2025
Silver League
9360 points
Build a Certification Study Guide: PCSE Exam Prep
Earned Aug 20, 2025 EDT
Google Cloud Fundamentals: Core Infrastructure
Earned Aug 20, 2025 EDT
DEPRECATED Build and Deploy Machine Learning Solutions on Vertex AI
Earned Jul 6, 2025 EDT
Responsible AI for Developers: Privacy & Safety
Earned Jul 6, 2025 EDT
Introduction to Security in the World of AI
Earned Jul 6, 2025 EDT
Responsible AI for Developers: Interpretability & Transparency
Earned Jul 6, 2025 EDT
Responsible AI for Developers: Fairness & Bias
Earned Jul 5, 2025 EDT
Machine Learning Operations (MLOps) with Agent Platform: Model Evaluation
Earned Jul 5, 2025 EDT
Machine Learning Operations (MLOps) for Generative AI
Earned Jul 5, 2025 EDT
Build a Certification Study Guide: PMLE
Earned Apr 8, 2025 EDT
Learn how to use Gemini Notebook to create a personalized study guide for the Professional Cloud Security Engineer certification exam. You'll review Gemini Notebook features, create a notebook, and learn how to use a study guide to practice for the certification exam.
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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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Earn the intermediate skill badge by completing the Build and Deploy Machine Learning Solutions on Vertex AI skill badge course, where you learn how to use Google Cloud's Vertex AI platform, AutoML, and custom training services to train, evaluate, tune, explain, and deploy machine learning models.
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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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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 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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Learn how to use Gemini Notebook to create a personalized study guide for the Professional Machine Learning Engineer (PMLE) certification exam. You'll review Gemini Notebook features, create a notebook, and use the study guide to practice for a certification exam.
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