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Marvin Schubert

Member since 2021

Bronze League

3885 points
Gen AI Agents: Transform Your Organization Earned июля 22, 2025 EDT
Gen AI Apps: Transform Your Work Earned июля 15, 2025 EDT
Gen AI: Navigate the Landscape Earned июля 14, 2025 EDT
Gen AI: Unlock Foundational Concepts Earned июня 27, 2025 EDT
Gen AI: Beyond the Chatbot Earned июня 25, 2025 EDT
Generative AI for Business Leaders Earned окт. 19, 2023 EDT
Generative AI Explorer : Vertex AI Earned окт. 17, 2023 EDT
Introduction to Image Generation Earned сент. 6, 2023 EDT
Responsible AI: Applying AI Principles with Google Cloud Earned сент. 6, 2023 EDT
Generative AI Fundamentals Earned авг. 18, 2023 EDT
Introduction to Responsible AI Earned авг. 17, 2023 EDT
Introduction to Large Language Models Earned авг. 17, 2023 EDT
Introduction to Generative AI Earned авг. 4, 2023 EDT
End-to-End Machine Learning with TensorFlow on Google Cloud Earned февр. 10, 2022 EST
Machine Learning in the Enterprise - Locales Earned янв. 27, 2022 EST
Build, Train and Deploy ML Models with Keras on Google Cloud Earned янв. 17, 2022 EST
Feature Engineering Earned янв. 17, 2022 EST
Launching into Machine Learning Earned авг. 5, 2021 EDT
Google Cloud Big Data and Machine Learning Fundamentals Earned мая 28, 2021 EDT

Gen AI Agents: Transform Your Organization is the fifth and final course of the Gen AI Leader learning path. This course explores how organizations can use custom gen AI agents to help tackle specific business challenges. You gain hands-on practice building a basic gen AI agent, while exploring the components of these agents, such as models, reasoning loops, and tools.

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Transform Your Work With Gen AI Apps is the fourth course of the Gen AI Leader learning path. This course introduces Google’s gen AI applications, such as Google Workspace with Gemini and NotebookLM. It guides you through concepts like grounding, retrieval augmented generation, constructing effective prompts and building automated workflows.

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Gen AI: Navigate the Landscape s the third course of the Gen AI Leader learning path. Gen AI is changing how we work and interact with the world around us. But as a leader, how can you harness its power to drive real business outcomes? In this course, you explore the different layers of building gen AI solutions, Google Cloud’s offerings, and the factors to consider when selecting a solution.

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Gen AI: Unlock Foundational Concepts is the second course of the Gen AI Leader learning path. In this course, you unlock the foundational concepts of generative AI by exploring the differences between AI, ML, and gen AI, and understanding how various data types enable generative AI to address business challenges. You also gain insights into Google Cloud strategies to address the limitations of foundation models and the key challenges for responsible and secure AI development and deployment.

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Gen AI: Beyond the Chatbot is the first course of the Gen AI Leader learning path and has no prerequisites. This course aims to move beyond the basic understanding of chatbots to explore the true potential of generative AI for your organization. You explore concepts like foundation models and prompt engineering, which are crucial for leveraging the power of gen AI. The course also guides you through important considerations you should make when developing a successful gen AI strategy for your organization.

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A Business Leader in Generative AI can articulate the capabilities of core cloud Generative AI products and services and understand how they benefit organizations. This course provides an overview of the types of opportunities and challenges that companies often encounter in their digital transformation journey and how they can leverage Google Cloud's generative AI products to overcome these challenges.

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This content is deprecated. Please see the latest version of the course, here.

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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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Earn a skill badge by passing the final quiz, you'll demonstrate your understanding of foundational concepts in generative AI. A skill badge is a digital badge issued by Google Cloud in recognition of your knowledge of Google Cloud products and services. Share your skill badge by making your profile public and adding it to your social media profile.

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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.

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One of the best ways to review something is to work with the concepts and technologies that you have learned. So, this course is set up as a workshop and in this workshop, you will do End-to-End Machine Learning with TensorFlow on Google Cloud Platform. It involves building an end-to-end model from data exploration all the way to deploying an ML model and getting predictions from it. This is the first course of the Advanced Machine Learning on Google Cloud series. After completing this course, enroll in the Production Machine Learning Systems course.

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"This course, Machine Learning in the Enterprise - Locales, is intended for non-English learners. If you want to take this course in English, please enroll inMachine Learning in the Enterprise". This course encompasses a real-world practical approach to the ML Workflow: a case study approach that presents an ML team faced with several ML business requirements and use cases. This team must understand the tools required for data management and governance and consider the best approach for data preprocessing: from providing an overview of Dataflow and Dataprep to using BigQuery for preprocessing tasks. The team is presented with three options to build machine learning models for two specific use cases. This course explains why the team would use AutoML, BigQuery ML, or custom training to achieve their objectives. A deeper dive into custom training is presented in this course. We describe custom training requirements from training code structure, storage, and loading large datasets to e…

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This course covers building ML models with TensorFlow and Keras, improving the accuracy of ML models and writing ML models for scaled use.

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This course explores the benefits of using Vertex AI Feature Store, how to improve the accuracy of ML models, and how to find which data columns make the most useful features. This course also includes content and labs on feature engineering using BigQuery ML, Keras, and TensorFlow.

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The course begins with a discussion about data: how to improve data quality and perform exploratory data analysis. We describe Vertex AI AutoML and how to build, train, and deploy an ML model without writing a single line of code. You will understand the benefits of Big Query ML. We then discuss how to optimize a machine learning (ML) model and how generalization and sampling can help assess the quality of ML models for custom training.

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This course introduces the Google Cloud big data and machine learning products and services that support the data-to-AI lifecycle. It explores the processes, challenges, and benefits of building a big data pipeline and machine learning models with Vertex AI on Google Cloud.

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