Machine Learning Operations (MLOps) for Generative AI
Earned mai 10, 2024 EDT
Introduction to Image Generation
Earned mai 10, 2024 EDT
Responsible AI: Applying AI Principles with Google Cloud
Earned mai 10, 2024 EDT
Introduction to Responsible AI
Earned mai 10, 2024 EDT
Introduction to Large Language Models
Earned mai 10, 2024 EDT
Introduction to Data Analytics on Google Cloud
Earned mai 4, 2024 EDT
Introduction to Generative AI
Earned mai 3, 2024 EDT
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 course introduces diffusion models, a family of machine learning models that recently showed promise in the image generation space. Diffusion models draw inspiration from physics, specifically thermodynamics. Within the last few years, diffusion models became popular in both research and industry. Diffusion models underpin many state-of-the-art image generation models and tools on Google Cloud. This course introduces you to the theory behind diffusion models and how to train and deploy them on Vertex AI.
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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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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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In this beginner-level course, you will learn about the Data Analytics workflow on Google Cloud and the tools you can use to explore, analyze, and visualize data and share your findings with stakeholders. Using a case study along with hands-on labs, lectures, and quizzes/demos, the course will demonstrate how to go from raw datasets to clean data to impactful visualizations and dashboards. Whether you already work with data and want to learn how to be successful on Google Cloud, or you’re looking to progress in your career, this course will help you get started. Almost anyone who performs or uses data analysis in their work can benefit from this course.
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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.
Machine Learning Operations (MLOps) for Generative AI
Earned mai 10, 2024 EDT
Introduction to Image Generation
Earned mai 10, 2024 EDT
Responsible AI: Applying AI Principles with Google Cloud
Earned mai 10, 2024 EDT
Introduction to Responsible AI
Earned mai 10, 2024 EDT
Introduction to Large Language Models
Earned mai 10, 2024 EDT
Introduction to Data Analytics on Google Cloud
Earned mai 4, 2024 EDT
Introduction to Generative AI
Earned mai 3, 2024 EDT