Create Embeddings, Vector Search, and RAG with BigQuery
Earned juil. 23, 2025 EDT
Create ML Models with BigQuery ML
Earned juil. 22, 2025 EDT
Boost Productivity with Gemini in BigQuery
Earned juil. 17, 2025 EDT
Work with Gemini Models in BigQuery
Earned juil. 10, 2025 EDT
Using BigQuery Machine Learning for Inference
Earned juil. 3, 2025 EDT
Gemini for Data Scientists and Analysts
Earned juin 16, 2025 EDT
Introduction to AI and Machine Learning on Google Cloud
Earned juin 9, 2025 EDT
Build Generative AI Agents with Vertex AI and Flutter
Earned mai 17, 2025 EDT
Develop Gen AI Apps with Gemini and Streamlit
Earned mai 9, 2025 EDT
Website Modernization with Generative AI on Google Cloud
Earned mars 24, 2025 EDT
Create Generative AI Apps on Google Cloud
Earned mars 20, 2025 EDT
Gemini for end-to-end SDLC
Earned mars 17, 2025 EDT
Gemini for Application Developers
Earned mars 16, 2025 EDT
Introduction to Security in the World of AI
Earned mars 15, 2025 EDT
Machine Learning Operations (MLOps) with Vertex AI: Model Evaluation
Earned mars 13, 2025 EDT
Encoder-Decoder Architecture
Earned mars 10, 2025 EDT
Machine Learning Operations (MLOps) for Generative AI
Earned mars 9, 2025 EDT
Responsible AI for Developers: Privacy & Safety
Earned mars 8, 2025 EST
Responsible AI for Developers: Interpretability & Transparency
Earned fév. 20, 2025 EST
Responsible AI for Developers: Fairness & Bias
Earned fév. 18, 2025 EST
Inspect Rich Documents with Gemini Multimodality and Multimodal RAG
Earned fév. 17, 2025 EST
Vector Search and Embeddings
Earned fév. 15, 2025 EST
Introduction to Vertex AI Studio
Earned déc. 21, 2024 EST
Create Image Captioning Models
Earned déc. 9, 2024 EST
Transformer Models and BERT Model
Earned déc. 2, 2024 EST
Attention Mechanism
Earned nov. 23, 2024 EST
Introduction to Image Generation
Earned nov. 23, 2024 EST
Responsible AI: Applying AI Principles with Google Cloud
Earned nov. 17, 2024 EST
Prompt Design in Vertex AI
Earned nov. 17, 2024 EST
Introduction to Responsible AI
Earned nov. 16, 2024 EST
Introduction to Large Language Models
Earned nov. 16, 2024 EST
Introduction to Generative AI
Earned nov. 16, 2024 EST
Gemini for Google Workspace provides customers with generative AI features in Google Workspace. In this mini-course, you learn about the key features of Gemini and how they can be used to improve productivity and efficiency in Gmail.
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Gemini for Google Workspace provides customers with generative AI features in Google Workspace. In this learning path, you learn about the key features of Gemini and how they can be used to improve productivity and efficiency in Google Workspace.
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In this course, you learn how Gemini, a generative AI-powered collaborator from Google Cloud, helps network engineers create, update, and maintain VPC networks. You learn how to prompt Gemini to provide specific guidance for your networking tasks, beyond what you would receive from a search engine. Using a hands-on lab, you experience how Gemini makes it easier for you to work with Google Cloud VPC networks.
Duet AI was renamed to Gemini, our next-generation model.
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In this course, you learn how Gemini, a generative AI-powered collaborator from Google Cloud, helps administrators provision infrastructure. You learn how to prompt Gemini to explain infrastructure, deploy GKE clusters and update existing infrastructure. Using a hands-on lab, you experience how Gemini improves the GKE deployment workflow.
Duet AI was renamed to Gemini, our next-generation model.
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Complete the intermediate Implement Multimodal Vector Search with BigQuery skill badge to demonstrate skills in the following: using Gemini in BigQuery to generate and debug SQL, conduct sentiment analysis, summarize text and identify keywords, generate embeddings, create a Retrieval Augmented Generation (RAG) pipeline, and implement multimodal vector search. A skill badge is an exclusive digital badge issued by Google Cloud in recognition of your proficiency with Google Cloud products and services and tests your ability to apply your knowledge in an interactive hands-on environment. Complete this Skill Badge, and the final assessment challenge lab, to receive a digital badge that you can share with your network.
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This course explores a Retrieval Augmented Generation (RAG) solution in BigQuery to mitigate AI hallucinations. It introduces a RAG workflow that encompasses creating embeddings, searching a vector space, and generating improved answers. The course explains the conceptual reasons behind these steps and their practical implementation with BigQuery. By the end of the course, learners will be able to build a RAG pipeline using BigQuery and generative AI models like Gemini and embedding models to address their own AI hallucination use cases.
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Complete the intermediate Create ML Models with BigQuery ML skill badge to demonstrate skills in creating and evaluating machine learning models with BigQuery ML to make data predictions.
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This course explores Gemini in BigQuery, a suite of AI-driven features to assist data-to-AI workflow. These features include data exploration and preparation, code generation and troubleshooting, and workflow discovery and visualization. Through conceptual explanations, a practical use case, and hands-on labs, the course empowers data practitioners to boost their productivity and expedite the development pipeline.
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This course demonstrates how to use AI/ML models for generative AI tasks in BigQuery. Through a practical use case involving customer relationship management, you learn the workflow of solving a business problem with Gemini models. To facilitate comprehension, the course also provides step-by-step guidance through coding solutions using both SQL queries and Python notebooks.
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Learn about BigQuery ML for Inference, why Data Analysts should use it, its use cases, and supported ML models. You will also learn how to create and manage these ML models in BigQuery.
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In this course, you learn how Gemini, a generative AI-powered collaborator from Google Cloud, helps analyze customer data and predict product sales. You also learn how to identify, categorize, and develop new customers using customer data in BigQuery. Using hands-on labs, you experience how Gemini improves data analysis and machine learning workflows.
Duet AI was renamed to Gemini, our next-generation model.
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This course introduces the AI and machine learning (ML) offerings on Google Cloud that build both predictive and generative AI projects. It explores the technologies, products, and tools available throughout the data-to-AI life cycle, encompassing AI foundations, development, and solutions. It aims to help data scientists, AI developers, and ML engineers enhance their skills and knowledge through engaging learning experiences and practical hands-on exercises.
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In this course, you learn how to develop an app using Flutter, Google's portable UI toolkit, and integrate the app with Gemini, Google's family of generative AI models. You also use Vertex AI Agent Builder, Google's platform for building and managing AI Agents and applications.
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Complete the intermediate Develop Gen AI Apps with Gemini and Streamlit skill badge course to demonstrate skills in text generation, applying function calls with the Python SDK and Gemini API, and deploying a Streamlit application with Cloud Run. In this course, you learn Gemini prompting, test Streamlit apps in Cloud Shell, and deploy them as Docker containers in Cloud Run.
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Enhance the navigation experience of your website by using generative AI to provide a better search experience for your users. In this course, you learn how to use Vertex AI Search to provide your website users a generative search experience enabling them to discover content offered by the website. As a website editor, you also learn how to use generative AI to quickly and efficiently translate and improve the content using suggestions.
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Generative AI applications can create new user experiences that were nearly impossible before the invention of large language models (LLMs). As an application developer, how can you use generative AI to build engaging, powerful apps on Google Cloud?
In this course, you'll learn about generative AI applications and how you can use prompt design and retrieval augmented generation (RAG) to build powerful applications using LLMs. You'll learn about a production-ready architecture that can be used for generative AI applications and you'll build an LLM and RAG-based chat application.
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In this course, you learn how Gemini, a generative AI-powered collaborator from Google Cloud, helps you use Google products and services to develop, test, deploy, and manage applications. With help from Gemini, you learn how to develop and build a web application, fix errors in the application, develop tests, and query data. Using a hands-on lab, you experience how Gemini improves the software development lifecycle (SDLC).
Duet AI was renamed to Gemini, our next-generation model.
En savoir plus
In this course, you learn how Gemini, a generative AI-powered collaborator from Google Cloud, helps developers build applications. You learn how to prompt Gemini to explain code, recommend Google Cloud services, and generate code for your applications. Using a hands-on lab, you experience how Gemini improves the application development workflow.
Duet AI was renamed to Gemini, our next-generation model.
En savoir plus
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 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 gives you a synopsis of the encoder-decoder architecture, which is a powerful and prevalent machine learning architecture for sequence-to-sequence tasks such as machine translation, text summarization, and question answering. You learn about the main components of the encoder-decoder architecture and how to train and serve these models. In the corresponding lab walkthrough, you’ll code in TensorFlow a simple implementation of the encoder-decoder architecture for poetry generation from the beginning.
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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 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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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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Complete the intermediate Inspect Rich Documents with Gemini Multimodality and Multimodal RAG skill badge to demonstrate skills in the following: using multimodal prompts to extract information from text and visual data, generating a video description, and retrieving extra information beyond the video using multimodality with Gemini; building metadata of documents containing text and images, getting all relevant text chunks, and printing citations by using Multimodal Retrieval Augmented Generation (RAG) with Gemini. A skill badge is an exclusive digital badge issued by Google Cloud in recognition of your proficiency with Google Cloud products and services and tests your ability to apply your knowledge in an interactive hands-on environment. Complete this skill badge course and the final assessment challenge lab to receive a skill badge that you can share with your network.
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Explore AI-powered search technologies, tools, and applications in this course. Learn semantic search utilizing vector embeddings, hybrid search combining semantic and keyword approaches, and retrieval-augmented generation (RAG) minimizing AI hallucinations as a grounded AI agent. Gain practical experience with Vertex AI Vector Search to build your intelligent search engine.
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This course introduces Vertex AI Studio, a tool to interact with generative AI models, prototype business ideas, and launch them into production. Through an immersive use case, engaging lessons, and a hands-on lab, you’ll explore the prompt-to-product lifecycle and learn how to leverage Vertex AI Studio for Gemini multimodal applications, prompt design, prompt engineering, and model tuning. The aim is to enable you to unlock the potential of gen AI in your projects with Vertex AI Studio.
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This course teaches you how to create an image captioning model by using deep learning. You learn about the different components of an image captioning model, such as the encoder and decoder, and how to train and evaluate your model. By the end of this course, you will be able to create your own image captioning models and use them to generate captions for images
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This course introduces you to the Transformer architecture and the Bidirectional Encoder Representations from Transformers (BERT) model. You learn about the main components of the Transformer architecture, such as the self-attention mechanism, and how it is used to build the BERT model. You also learn about the different tasks that BERT can be used for, such as text classification, question answering, and natural language inference.This course is estimated to take approximately 45 minutes to complete.
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This course will introduce you to the attention mechanism, a powerful technique that allows neural networks to focus on specific parts of an input sequence. You will learn how attention works, and how it can be used to improve the performance of a variety of machine learning tasks, including machine translation, text summarization, and question answering. This course is estimated to take approximately 45 minutes to complete.
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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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Complete the introductory Prompt Design in Vertex AI skill badge to demonstrate skills in the following: prompt engineering, image analysis, and multimodal generative techniques, within Vertex AI. Discover how to craft effective prompts, guide generative AI output, and apply Gemini models to real-world marketing scenarios.
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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.
Gemini in Gmail
Earned août 5, 2025 EDT
Introduction to Gemini for Google Workspace
Earned août 5, 2025 EDT
Gemini for Network Engineers
Earned juil. 26, 2025 EDT
Gemini for Cloud Architects
Earned juil. 25, 2025 EDT
Implement Multimodal Vector Search with BigQuery
Earned juil. 24, 2025 EDT
Create Embeddings, Vector Search, and RAG with BigQuery
Earned juil. 23, 2025 EDT
Create ML Models with BigQuery ML
Earned juil. 22, 2025 EDT
Boost Productivity with Gemini in BigQuery
Earned juil. 17, 2025 EDT
Work with Gemini Models in BigQuery
Earned juil. 10, 2025 EDT
Using BigQuery Machine Learning for Inference
Earned juil. 3, 2025 EDT
Gemini for Data Scientists and Analysts
Earned juin 16, 2025 EDT
Introduction to AI and Machine Learning on Google Cloud
Earned juin 9, 2025 EDT
Build Generative AI Agents with Vertex AI and Flutter
Earned mai 17, 2025 EDT
Develop Gen AI Apps with Gemini and Streamlit
Earned mai 9, 2025 EDT
Website Modernization with Generative AI on Google Cloud
Earned mars 24, 2025 EDT
Create Generative AI Apps on Google Cloud
Earned mars 20, 2025 EDT
Gemini for end-to-end SDLC
Earned mars 17, 2025 EDT
Gemini for Application Developers
Earned mars 16, 2025 EDT
Introduction to Security in the World of AI
Earned mars 15, 2025 EDT
Machine Learning Operations (MLOps) with Vertex AI: Model Evaluation
Earned mars 13, 2025 EDT
Encoder-Decoder Architecture
Earned mars 10, 2025 EDT
Machine Learning Operations (MLOps) for Generative AI
Earned mars 9, 2025 EDT
Responsible AI for Developers: Privacy & Safety
Earned mars 8, 2025 EST
Responsible AI for Developers: Interpretability & Transparency
Earned fév. 20, 2025 EST
Responsible AI for Developers: Fairness & Bias
Earned fév. 18, 2025 EST
Inspect Rich Documents with Gemini Multimodality and Multimodal RAG
Earned fév. 17, 2025 EST
Vector Search and Embeddings
Earned fév. 15, 2025 EST
Introduction to Vertex AI Studio
Earned déc. 21, 2024 EST
Create Image Captioning Models
Earned déc. 9, 2024 EST
Transformer Models and BERT Model
Earned déc. 2, 2024 EST
Attention Mechanism
Earned nov. 23, 2024 EST
Introduction to Image Generation
Earned nov. 23, 2024 EST
Responsible AI: Applying AI Principles with Google Cloud
Earned nov. 17, 2024 EST
Prompt Design in Vertex AI
Earned nov. 17, 2024 EST
Introduction to Responsible AI
Earned nov. 16, 2024 EST
Introduction to Large Language Models
Earned nov. 16, 2024 EST
Introduction to Generative AI
Earned nov. 16, 2024 EST