Create Engaging Video with Google Vids
Earned fév. 13, 2026 EST
Gemini in Google Drive
Earned fév. 13, 2026 EST
Gemini in Google Meet
Earned fév. 13, 2026 EST
Gemini in Google Sheets
Earned fév. 13, 2026 EST
Gemini in Google Slides
Earned fév. 13, 2026 EST
Gemini in Google Docs
Earned fév. 13, 2026 EST
Gemini in Gmail
Earned fév. 13, 2026 EST
Introduction to Google Workspace with Gemini
Earned fév. 13, 2026 EST
Gemini for Application Developers
Earned fév. 13, 2026 EST
Gemini for end-to-end SDLC
Earned jan. 19, 2026 EST
Inspect Rich Documents with Gemini Multimodality and Multimodal RAG
Earned jan. 18, 2026 EST
Vector Search and Embeddings
Earned jan. 17, 2026 EST
Configure Service Accounts and IAM Roles for Google Cloud
Earned jan. 17, 2026 EST
Networking in Google Cloud: Fundamentals
Earned jan. 17, 2026 EST
Logging and Monitoring in Google Cloud
Earned jan. 17, 2026 EST
Build Data Lakes and Data Warehouses on Google Cloud
Earned jan. 17, 2026 EST
Reliable Google Cloud Infrastructure: Design and Process
Earned jan. 14, 2026 EST
Google Cloud Computing Foundations: Cloud Computing Fundamentals
Earned jan. 2, 2026 EST
Introduction to Vertex AI Studio
Earned jan. 1, 2026 EST
Create Image Captioning Models
Earned déc. 31, 2025 EST
Transformer Models and BERT Model
Earned déc. 31, 2025 EST
Model Armor: Securing AI Deployments
Earned déc. 31, 2025 EST
Introduction to Security in the World of AI
Earned déc. 30, 2025 EST
Responsible AI for Developers: Interpretability & Transparency
Earned déc. 30, 2025 EST
Responsible AI for Developers: Privacy & Safety
Earned déc. 30, 2025 EST
Machine Learning Operations (MLOps) with Vertex AI: Manage Features
Earned déc. 29, 2025 EST
Production Machine Learning Systems
Earned déc. 29, 2025 EST
Build, Train and Deploy ML Models with Keras on Google Cloud
Earned déc. 29, 2025 EST
Encoder-Decoder Architecture
Earned déc. 29, 2025 EST
Attention Mechanism
Earned déc. 29, 2025 EST
Introduction to Image Generation
Earned déc. 29, 2025 EST
AI Infrastructure: Networking Techniques
Earned déc. 29, 2025 EST
AI Infrastructure: Storage Options
Earned déc. 29, 2025 EST
AI Infrastructure: Deployment Types
Earned déc. 28, 2025 EST
AI Infrastructure: Cloud TPUs
Earned déc. 27, 2025 EST
AI Infrastructure: Cloud GPUs
Earned déc. 27, 2025 EST
AI Infrastructure: Introduction to AI Hypercomputer
Earned déc. 26, 2025 EST
Machine Learning Operations (MLOps): Getting Started
Earned déc. 21, 2025 EST
Engineer Data for Predictive Modeling with BigQuery ML
Earned déc. 19, 2025 EST
Feature Engineering
Earned déc. 18, 2025 EST
Create ML Models with BigQuery ML
Earned déc. 18, 2025 EST
Working with Notebooks in Vertex AI
Earned déc. 15, 2025 EST
Responsible AI for Developers: Fairness & Bias
Earned déc. 15, 2025 EST
Create Generative AI Apps on Google Cloud
Earned déc. 15, 2025 EST
Set Up an App Dev Environment on Google Cloud
Earned déc. 15, 2025 EST
Google Cloud Fundamentals: Core Infrastructure
Earned déc. 14, 2025 EST
Introduction to AI and Machine Learning on Google Cloud
Earned déc. 14, 2025 EST
Implementing Cloud Load Balancing for Compute Engine
Earned déc. 14, 2025 EST
Machine Learning Operations (MLOps) with Vertex AI: Model Evaluation
Earned déc. 12, 2025 EST
Machine Learning Operations (MLOps) for Generative AI
Earned déc. 12, 2025 EST
Responsible AI: Applying AI Principles with Google Cloud
Earned déc. 11, 2025 EST
Prompt Design in Vertex AI
Earned déc. 11, 2025 EST
Generative AI Explorer - Vertex AI
Earned déc. 11, 2025 EST
Prepare Data for ML APIs on Google Cloud
Earned déc. 10, 2025 EST
Build a Certification Study Guide: PMLE
Earned déc. 8, 2025 EST
Gen AI Agents: Transform Your Organization
Earned déc. 7, 2025 EST
Gen AI Apps: Transform Your Work
Earned déc. 6, 2025 EST
Gen AI: Navigate the Landscape
Earned déc. 5, 2025 EST
Gen AI: Unlock Foundational Concepts
Earned déc. 5, 2025 EST
Gen AI: Beyond the Chatbot
Earned déc. 4, 2025 EST
Scaling with Google Cloud Operations
Earned déc. 4, 2025 EST
Trust and Security with Google Cloud
Earned déc. 4, 2025 EST
Modernize Infrastructure and Applications with Google Cloud
Earned déc. 3, 2025 EST
Innovating with Google Cloud Artificial Intelligence
Earned déc. 3, 2025 EST
Exploring Data Transformation with Google Cloud
Earned déc. 2, 2025 EST
Digital Transformation with Google Cloud
Earned déc. 2, 2025 EST
Preparing for Your Associate Cloud Engineer Journey
Earned août 26, 2025 EDT
Introduction to Responsible AI
Earned août 12, 2025 EDT
Introduction to Large Language Models
Earned août 8, 2025 EDT
Introduction to Generative AI
Earned août 7, 2025 EDT
In this course, you'll learn about Google Vids, an online video creation and editing app available to select Google Workspace users. Through lessons and demos, you'll learn how to build and tell compelling stories through video at work. You'll also discover how to seamlessly incorporate media, audio and video clips, customize styles, and easily share your creations.
Some Vids features use generative AI to help you work more efficiently. Remember, generative AI tools including Gemini, may suggest inaccurate or inappropriate information. Don’t rely on Gemini features as medical, legal, financial or other professional advice. It’s also important to remember that the Gemini feature suggestions don’t represent Google’s views, and should not be attributed to Google.
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Google Workspace with Gemini provides customers with access to generative AI features. This course delves into the capabilities of Gemini in Google Drive using video lessons, hands-on activities and practical examples. By the end of this course, you'll be equipped with the knowledge and skills to confidently utilize Gemini in Google Drive to improve your workflows.
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Google Workspace with Gemini provides customers with access to generative AI features. This course delves into the capabilities of Gemini in Google Meet. Through video lessons, hands-on activities and practical examples, you will gain a comprehensive understanding of the Gemini features in Google Meet. You learn how to use Gemini to generate background images, improve your video quality, and translate captions. By the end of this course, you'll be equipped with the knowledge and skills to confidently utilize Gemini in Google Meet to maximize the effectiveness of your video conferences.
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Google Workspace with Gemini 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 Google Sheets.
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Google Workspace with Gemini 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 Google Slides.
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Google Workspace with Gemini provides customers with access to generative AI features. This course delves into the capabilities of Gemini in Google Docs using video lessons, hands-on activities and practical examples. You learn how to use Gemini to generate written content based on prompts. You also explore using Gemini to edit text you’ve already written, helping you improve your overall productivity. By the end of this course, you'll be equipped with the knowledge and skills to confidently utilize Gemini in Google Docs to improve your writing.
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Google Workspace with Gemini 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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Google Workspace with Gemini 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 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.
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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.
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Complete the intermediate Inspect Rich Documents with Gemini Multimodality and Multimodal RAG skill badge course 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.
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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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Earn the Introductory skill badge by completing the Configure Service Accounts and IAM Roles for Google Cloud course, where you learn about service accounts, custom roles, and how to set permissions using gcloud .
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Networking in Google cloud is a 6 part course series. Welcome to the first course of our six part course series, Networking in Google Cloud: Fundamentals.
This course provides a comprehensive overview of core networking concepts, including networking fundamentals, virtual private clouds (VPCs), and the sharing of VPC networks. Additionally, the course covers network logging and monitoring techniques.
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Welcome to the two-part course on Logging, Monitoring, and Observability in Google Cloud. The core operations tools in Google Cloud break down into two major categories. The operations-focused components and the application performance management tools. This course, Logging and Monitoring in Google Cloud, covers the operations-focused components including Logging, Monitoring, and Service Monitoring. After taking this course, it is suggested that you complete part 2, Observability in Google Cloud, to learn about the available application performance management tools.
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While the traditional approaches of using data lakes and data warehouses can be effective, they have shortcomings, particularly in large enterprise environments. This course introduces the concept of a data lakehouse and the Google Cloud products used to create one. A lakehouse architecture uses open-standard data sources and combines the best features of data lakes and data warehouses, which addresses many of their shortcomings.
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This course equips students to build highly reliable and efficient solutions on Google Cloud using proven design patterns. It is a continuation of the Architecting with Google Compute Engine or Architecting with Google Kubernetes Engine courses and assumes hands-on experience with the technologies covered in either of those courses. Through a combination of presentations, design activities, and hands-on labs, participants learn to define and balance business and technical requirements to design Google Cloud deployments that are highly reliable, highly available, secure, and cost-effective.
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The Google Cloud Computing Foundations courses are for individuals with little to no background or experience in cloud computing. They provide an overview of concepts central to cloud basics, big data, and machine learning, and where and how Google Cloud fits in.
By the end of the series of courses, learners will be able to articulate these concepts and demonstrate some hands-on skills.
The courses should be completed in the following order:
1. Google Cloud Computing Foundations: Cloud Computing Fundamentals
2. Google Cloud Computing Foundations: Infrastructure in Google Cloud
3. Google Cloud Computing Foundations: Networking and Security in Google Cloud
4. Google Cloud Computing Foundations: Data, ML, and AI in Google Cloud
This first course provides an overview of cloud computing, ways to use Google Cloud, and different compute options.
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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 reviews the essential security features of Model Armor and equips you to work with the service. You’ll learn about the security risks associated with LLMs and how Model Armor protects your AI applications.
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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 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 participants to MLOps tools and best practices for deploying, evaluating, monitoring and operating production ML systems on Google Cloud. MLOps is a discipline focused on the deployment, testing, monitoring, and automation of ML systems in production.
Learners will get hands-on practice using Vertex AI Feature Store's streaming ingestion at the SDK layer.
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This course covers how to implement the various flavors of production ML systems— static, dynamic, and continuous training; static and dynamic inference; and batch and online processing. You delve into TensorFlow abstraction levels, the various options for doing distributed training, and how to write distributed training models with custom estimators.
This is the second course of the Advanced Machine Learning on Google Cloud series. After completing this course, enroll in the Image Understanding with TensorFlow on Google Cloud course.
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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 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 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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Welcome to the "AI Infrastructure: Networking Techniques" course. In this course, you'll learn to leverage Google Cloud's high-bandwidth, low-latency infrastructure to optimize data transfer and communication between all the components of your AI system. By the end, you'll grasp the critical role networking plays across the entire AI pipeline from data ingestion and training to inference and be able to apply best practices to ensure your workloads run at maximum speed.
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In this course, you’ll take a comprehensive journey through the storage solutions available on Google Cloud, specifically tailored for AI and high-performance computing (HPC) workloads. You’ll learn how to choose the right storage for each stage of the ML lifecycle. You’ll explore how to optimize for I/O performance during training, manage massive datasets for data preparation, and serve model artifacts with low latency. Through practical examples and demonstrations, you’ll gain the expertise to design robust storage solutions that accelerate your AI innovation.
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This course provides a comprehensive guide to deploying, managing, and optimizing AI and high-performance computing (HPC) workloads on Google Cloud. Through a series of lessons and practical demonstrations, you’ll explore diverse deployment strategies, ranging from highly customizable environments using Google Compute Engine (GCE) to managed solutions like Google Kubernetes Engine (GKE). Specifically, you’ll learn how to create clusters and deploy GKE for inference.
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Welcome to the Cloud TPUs course. We'll explore the advantages and disadvantages of TPUs in various scenarios and compare different TPU accelerators to help you choose the right fit. You'll learn strategies to maximize performance and efficiency for your AI models and understand the significance of GPU/TPU interoperability for flexible machine learning workflows. Through engaging content and practical demos, we'll guide you step-by-step in leveraging TPUs effectively.
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Curious about the powerful hardware behind AI? This module breaks down performance-optimized AI computers, showing you why they're so important. We'll explore how CPUs, GPUs, and TPUs make AI tasks super fast, what makes each one unique, and how AI software gets the most out of them. By the end, you'll know exactly how to pick the right GPU for your AI projects, helping you make smart choices for your AI workloads.
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Ready to get started with AI Hypercomputers? This course makes it easy! We'll cover the basics of what they are and how they help AI with AI workloads. You'll learn about the different components inside a hypercomputer, like GPUs, TPUs, and CPUs, and discover how to pick the right deployment approach for your needs.
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This course introduces participants to MLOps tools and best practices for deploying, evaluating, monitoring and operating production ML systems on Google Cloud. MLOps is a discipline focused on the deployment, testing, monitoring, and automation of ML systems in production. Machine Learning Engineering professionals use tools for continuous improvement and evaluation of deployed models. They work with (or can be) Data Scientists, who develop models, to enable velocity and rigor in deploying the best performing models.
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Complete the intermediate Engineer Data for Predictive Modeling with BigQuery ML skill badge to demonstrate skills in the following: building data transformation pipelines to BigQuery using Dataprep by Trifacta; using Cloud Storage, Dataflow, and BigQuery to build extract, transform, and load (ETL) workflows; and building machine learning models using BigQuery ML.
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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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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 is an introduction to Vertex AI Notebooks, which are Jupyter notebook-based environments that provide a unified platform for the entire machine learning workflow, from data preparation to model deployment and monitoring. The course covers the following topics: (1) The different types of Vertex AI Notebooks and their features and (2) How to create and manage Vertex AI Notebooks.
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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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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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Earn a skill badge by completing the Set Up an App Dev Environment on Google Cloud skill badge course, where you learn how to build and connect storage-centric cloud infrastructure using the basic capabilities of the following technologies: Cloud Storage, Identity and Access Management, Cloud Functions, and Pub/Sub.
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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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This course introduces Google Cloud's AI and machine learning (ML) capabilities, with a focus on developing both generative and predictive AI projects. It explores the various technologies, products, and tools available throughout the data-to-AI lifecycle, empowering data scientists, AI developers, and ML engineers to enhance their expertise through interactive exercises.
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Complete the introductory Implementing Cloud Load Balancing for Compute Engine skill badge to demonstrate skills in the following: creating and deploying virtual machines in Compute Engine and configuring network and application load balancers.
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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 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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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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The Generative AI Explorer - Vertex Quest is a collection of labs on how to use Generative AI on Google Cloud. Through the labs, you will learn about how to use the models in the Vertex AI PaLM API family, including text-bison, chat-bison, and textembedding-gecko. You will also learn about prompt design, best practices, and how it can be used for ideation, text classification, text extraction, text summarization, and more. You will also learn how to tune a foundation model by training it via Vertex AI custom training and deploy it to a Vertex AI endpoint.
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Complete the introductory Prepare Data for ML APIs on Google Cloud skill badge to demonstrate skills in the following: cleaning data with Dataprep by Trifacta, running data pipelines in Dataflow, creating clusters and running Apache Spark jobs in Dataproc, and calling ML APIs including the Cloud Natural Language API, Google Cloud Speech-to-Text API, and Video Intelligence API.
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Learn how to use NotebookLM to create a personalized study guide for the Professional Machine Learning Engineer certification exam (PMLE). You'll review NotebookLM features, create a notebook, and use the study guide to practice for a certification exam.
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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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Organizations of all sizes are embracing the power and flexibility of the cloud to transform how they operate. However, managing and scaling cloud resources effectively can be a complex task.
Scaling with Google Cloud Operations explores the fundamental concepts of modern operations, reliability, and resilience in the cloud, and how Google Cloud can help support these efforts.
Part of the Cloud Digital Leader learning path, this course aims to help individuals grow in their role and build the future of their business.
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As organizations move their data and applications to the cloud, they must address new security challenges. The Trust and Security with Google Cloud course explores the basics of cloud security, the value of Google Cloud's multilayered approach to infrastructure security, and how Google earns and maintains customer trust in the cloud.
Part of the Cloud Digital Leader learning path, this course aims to help individuals grow in their role and build the future of their business.
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Many traditional enterprises use legacy systems and applications that can't stay up-to-date with modern customer expectations. Business leaders often have to choose between maintaining their aging IT systems or investing in new products and services. "Modernize Infrastructure and Applications with Google Cloud" explores these challenges and offers solutions to overcome them by using cloud technology.
Part of the Cloud Digital Leader learning path, this course aims to help individuals grow in their role and build the future of their business.
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Artificial intelligence (AI) and machine learning (ML) represent an important evolution in information technologies that are quickly transforming a wide range of industries. “Innovating with Google Cloud Artificial Intelligence” explores how organizations can use AI and ML to transform their business processes.
Part of the Cloud Digital Leader learning path, this course aims to help individuals grow in their role and build the future of their business.
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Cloud technology can bring great value to an organization, and combining the power of cloud technology with data has the potential to unlock even more value and create new customer experiences.
“Exploring Data Transformation with Google Cloud” explores the value data can bring to an organization and ways Google Cloud can make data useful and accessible.
Part of the Cloud Digital Leader learning path, this course aims to help individuals grow in their role and build the future of their business.
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There's much excitement about cloud technology and digital transformation, but often many unanswered questions.
For example: What is cloud technology? What does digital transformation mean? How can cloud technology help your organization? Where do you even begin?
If you've asked yourself any of these questions, you're in the right place. This course provides an overview of the types of opportunities and challenges that companies often encounter in their digital transformation journey. If you want to learn about cloud technology so you can excel in your role and help build the future of your business, then this introductory course on digital transformation is for you. This course is part of the Cloud Digital Leader learning path.
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This course helps you structure your preparation for the Associate Cloud Engineer exam. You will learn about the Google Cloud domains covered by the exam and how to create a study plan to improve your domain knowledge.
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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.
Create Engaging Video with Google Vids
Earned fév. 13, 2026 EST
Gemini in Google Drive
Earned fév. 13, 2026 EST
Gemini in Google Meet
Earned fév. 13, 2026 EST
Gemini in Google Sheets
Earned fév. 13, 2026 EST
Gemini in Google Slides
Earned fév. 13, 2026 EST
Gemini in Google Docs
Earned fév. 13, 2026 EST
Gemini in Gmail
Earned fév. 13, 2026 EST
Introduction to Google Workspace with Gemini
Earned fév. 13, 2026 EST
Gemini for Application Developers
Earned fév. 13, 2026 EST
Gemini for end-to-end SDLC
Earned jan. 19, 2026 EST
Inspect Rich Documents with Gemini Multimodality and Multimodal RAG
Earned jan. 18, 2026 EST
Vector Search and Embeddings
Earned jan. 17, 2026 EST
Configure Service Accounts and IAM Roles for Google Cloud
Earned jan. 17, 2026 EST
Networking in Google Cloud: Fundamentals
Earned jan. 17, 2026 EST
Logging and Monitoring in Google Cloud
Earned jan. 17, 2026 EST
Build Data Lakes and Data Warehouses on Google Cloud
Earned jan. 17, 2026 EST
Reliable Google Cloud Infrastructure: Design and Process
Earned jan. 14, 2026 EST
Google Cloud Computing Foundations: Cloud Computing Fundamentals
Earned jan. 2, 2026 EST
Introduction to Vertex AI Studio
Earned jan. 1, 2026 EST
Create Image Captioning Models
Earned déc. 31, 2025 EST
Transformer Models and BERT Model
Earned déc. 31, 2025 EST
Model Armor: Securing AI Deployments
Earned déc. 31, 2025 EST
Introduction to Security in the World of AI
Earned déc. 30, 2025 EST
Responsible AI for Developers: Interpretability & Transparency
Earned déc. 30, 2025 EST
Responsible AI for Developers: Privacy & Safety
Earned déc. 30, 2025 EST
Machine Learning Operations (MLOps) with Vertex AI: Manage Features
Earned déc. 29, 2025 EST
Production Machine Learning Systems
Earned déc. 29, 2025 EST
Build, Train and Deploy ML Models with Keras on Google Cloud
Earned déc. 29, 2025 EST
Encoder-Decoder Architecture
Earned déc. 29, 2025 EST
Attention Mechanism
Earned déc. 29, 2025 EST
Introduction to Image Generation
Earned déc. 29, 2025 EST
AI Infrastructure: Networking Techniques
Earned déc. 29, 2025 EST
AI Infrastructure: Storage Options
Earned déc. 29, 2025 EST
AI Infrastructure: Deployment Types
Earned déc. 28, 2025 EST
AI Infrastructure: Cloud TPUs
Earned déc. 27, 2025 EST
AI Infrastructure: Cloud GPUs
Earned déc. 27, 2025 EST
AI Infrastructure: Introduction to AI Hypercomputer
Earned déc. 26, 2025 EST
Machine Learning Operations (MLOps): Getting Started
Earned déc. 21, 2025 EST
Engineer Data for Predictive Modeling with BigQuery ML
Earned déc. 19, 2025 EST
Feature Engineering
Earned déc. 18, 2025 EST
Create ML Models with BigQuery ML
Earned déc. 18, 2025 EST
Working with Notebooks in Vertex AI
Earned déc. 15, 2025 EST
Responsible AI for Developers: Fairness & Bias
Earned déc. 15, 2025 EST
Create Generative AI Apps on Google Cloud
Earned déc. 15, 2025 EST
Set Up an App Dev Environment on Google Cloud
Earned déc. 15, 2025 EST
Google Cloud Fundamentals: Core Infrastructure
Earned déc. 14, 2025 EST
Introduction to AI and Machine Learning on Google Cloud
Earned déc. 14, 2025 EST
Implementing Cloud Load Balancing for Compute Engine
Earned déc. 14, 2025 EST
Machine Learning Operations (MLOps) with Vertex AI: Model Evaluation
Earned déc. 12, 2025 EST
Machine Learning Operations (MLOps) for Generative AI
Earned déc. 12, 2025 EST
Responsible AI: Applying AI Principles with Google Cloud
Earned déc. 11, 2025 EST
Prompt Design in Vertex AI
Earned déc. 11, 2025 EST
Generative AI Explorer - Vertex AI
Earned déc. 11, 2025 EST
Prepare Data for ML APIs on Google Cloud
Earned déc. 10, 2025 EST
Build a Certification Study Guide: PMLE
Earned déc. 8, 2025 EST
Gen AI Agents: Transform Your Organization
Earned déc. 7, 2025 EST
Gen AI Apps: Transform Your Work
Earned déc. 6, 2025 EST
Gen AI: Navigate the Landscape
Earned déc. 5, 2025 EST
Gen AI: Unlock Foundational Concepts
Earned déc. 5, 2025 EST
Gen AI: Beyond the Chatbot
Earned déc. 4, 2025 EST
Scaling with Google Cloud Operations
Earned déc. 4, 2025 EST
Trust and Security with Google Cloud
Earned déc. 4, 2025 EST
Modernize Infrastructure and Applications with Google Cloud
Earned déc. 3, 2025 EST
Innovating with Google Cloud Artificial Intelligence
Earned déc. 3, 2025 EST
Exploring Data Transformation with Google Cloud
Earned déc. 2, 2025 EST
Digital Transformation with Google Cloud
Earned déc. 2, 2025 EST
Preparing for Your Associate Cloud Engineer Journey
Earned août 26, 2025 EDT
Introduction to Responsible AI
Earned août 12, 2025 EDT
Introduction to Large Language Models
Earned août 8, 2025 EDT
Introduction to Generative AI
Earned août 7, 2025 EDT