Rahul Goyal
Учасник із 2026
Encoder-Decoder Architecture
Earned черв. 19, 2026 EDT
Attention Mechanism
Earned черв. 10, 2026 EDT
Introduction to Image Generation
Earned черв. 10, 2026 EDT
Gemini for end-to-end SDLC
Earned черв. 3, 2026 EDT
Gemini for Application Developers
Earned черв. 1, 2026 EDT
Manage Agent Memory and State
Earned трав. 26, 2026 EDT
Add Agent Capabilities With Tools
Earned трав. 24, 2026 EDT
Optimize Agent Behavior
Earned трав. 18, 2026 EDT
Engineer AI Agents with Agent Development Kit (ADK)
Earned трав. 15, 2026 EDT
Підготовка даних для інтерфейсів API машинного навчання в Google Cloud
Earned трав. 13, 2026 EDT
Build Your First Agent with Agent Development Kit (ADK)
Earned трав. 8, 2026 EDT
Build Agents with Agent Development Kit (ADK)
Earned трав. 8, 2026 EDT
Introduction to AI and Machine Learning on Google Cloud
Earned квіт. 30, 2026 EDT
Налаштування середовища для розробки додатка в Google Cloud
Earned квіт. 22, 2026 EDT
Google Cloud Fundamentals: Core Infrastructure - Yкраїнська
Earned квіт. 16, 2026 EDT
Налаштування Cloud Load Balancing для Compute Engine
Earned бер. 30, 2026 EDT
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 Gemini Enterprise Agent Platform.
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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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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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You've built basic LLM agents that respond to queries—now let's make them stateful. Use session state to build agents that maintain context, remember user preferences, and provide personalized experiences. Transform agents from stateless responders to intelligent assistants.
Explore other content in the Gemini Agent Ready (GEAR) program.
Join the community forum for questions and discussions
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You’ve built agents with advanced configuration—now give them real-world capabilities. Equip agents with tools that enable searching the web, executing code, querying databases, and performing custom actions. Transform agents from intelligent responders into capable assistants that take action.
Explore other content in the Gemini Agent Ready (GEAR) program.
Join the community forum for questions and discussions
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You’ve built your first agent—now it’s time to take it further. In this course, you’ll advance your skills by learning how to turn a basic AI agent into a sophisticated, precise assistant—applying advanced instructions, model selection, planning capabilities, and structured output patterns.
Explore other content in the Gemini Agent Ready (GEAR) program.
Join the community forum for questions and discussions
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Complete the intermediate Engineer AI Agents with Agent Development Kit (ADK) skill badge by completing this course to demonstrate skills in the following: formulating real-world language model research problems; building a simple tokenizer; preparing a dataset for training a transformer language model; running the training loop of a small language model. Explore other content in the Gemini Enterprise Agent Ready (GEAR) program.
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Пройдіть вступний кваліфікаційний курс Підготовка даних для інтерфейсів API машинного навчання в Google Cloud, щоб продемонструвати свої навички щодо очистки даних за допомогою сервісу Dataprep by Trifacta, запуску конвеєрів даних у Dataflow, створення кластерів і запуску завдань Apache Spark у Managed Service for Apache Spark, а також виклику API машинного навчання, зокрема Cloud Natural Language API, Google Cloud Speech-to-Text API і Video Intelligence API.
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Turn your understanding of agents into practical reality by building, configuring, and running your first AI agent using Google’s Agent Development Kit (ADK).
In this hands-on course, you’ll set up a complete ADK development environment, create agents with both Python code and YAML configuration, and run them through multiple interfaces. You’ll also learn the core parameters that define agent behavior, taking what you learned in course 1 and applying it to working code.
Explore other content in the Gemini Agent Ready (GEAR) program.
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Learn about how you can use Agent Development Kit (ADK) to build complex, production-ready AI agents. This course covers ADK’s open-source framework, moving from simple prompt engineering to a code-first, structured software development approach suitable for enterprise-grade, multi-agent systems. Explore other content in the Gemini Agent Ready (GEAR) program.
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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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Щоб отримати кваліфікаційний значок, пройдіть курс Налаштування середовища для розробки додатка в Google Cloud. У ньому ви навчитеся створювати й підключати хмарну інфраструктуру, спрямовану на зберігання даних, за допомогою базових можливостей таких технологій, як Cloud Storage, система керування ідентифікацією і доступом, Cloud Functions та Pub/Sub.
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Курс "Знайомство з Google Cloud: основна інфраструктура" охоплює важливі поняття й терміни щодо використання Google Cloud. Переглядаючи відео й виконуючи практичні завдання, слухачі ознайомляться з різними сервісами Google Cloud для обчислень і зберігання даних, а також важливими ресурсами й інструментами для керування правилами. Крім того, вони зможуть їх порівнювати.
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Пройдіть вступний кваліфікаційний курс Налаштування Cloud Load Balancing для Compute Engine, щоб продемонструвати свої навички: створення й розгортання віртуальних машин у Compute Engine; налаштування мережі й розподілювачів навантаження додатків.
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