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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Building a Conversational Interface with Dialogflow CX
Building conversational interfaces will enable you to engage your constituents in delightful new ways. This section will provide an overview of Dialogflow CX, which provides a range of new capabilities, languages, and functions.
Scaling Equity and Access with Enterprise Translation Hub
Accurate, timely and cost effective document translation has long been a challenge to providing equitable and accessible service to all constituents. In this section, explore Google's Enterprise Translation solutions which provide scalable document translation leveraging best in class Artificial Intelligence and Machine Learning, while retaining the critical human in the loop for final post editing review.
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In this Google DeepMind course, you will learn the fundamentals of language models and gain a high-level understanding of the machine learning development pipeline. You will consider the strengths and limitations of traditional n-gram models and advanced transformer models. Practical coding labs will enable you to develop insights into how machine learning models work and how they can be used to generate text and identify patterns in language. Through real-world case studies, you will build an understanding around how research engineers operate. Drawing on these insights you will identify problems that you wish to tackle in your own community and consider how to leverage the power of machine learning responsibly to address these problems within a global and local context.
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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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Responsible AI for Developers: Privacy & Safety
Earned ديسمبر 29, 2025 EST
Responsible AI for Developers: Interpretability & Transparency
Earned ديسمبر 29, 2025 EST
Google Cloud AI and ML Solutions for the Public Sector
Earned ديسمبر 29, 2025 EST
Google DeepMind: 01 Build Your Own Small Language Model
Earned نوفمبر 17, 2025 EST
Responsible AI for Developers: Fairness & Bias
Earned نوفمبر 16, 2025 EST
Machine Learning Operations (MLOps) with Agent Platform: Model Evaluation
Earned نوفمبر 16, 2025 EST
Machine Learning Operations (MLOps) for Generative AI
Earned نوفمبر 16, 2025 EST