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Gabriel Villazan Impastato

Учасник із 2019

Implement Cloud Security Fundamentals on Google Cloud Earned лип. 14, 2026 EDT
Deploy Multi-Agent Architectures Earned лип. 10, 2026 EDT
Deploy Multi-Agent Systems with Gemini Enterprise Agent Platform Earned лип. 10, 2026 EDT
Gemini in Google Meet Earned квіт. 25, 2024 EDT
Gemini in Google Sheets Earned квіт. 25, 2024 EDT
Gemini in Google Docs Earned січ. 23, 2024 EST
Gemini in Google Meet Earned січ. 22, 2024 EST
Gemini in Google Slides Earned січ. 22, 2024 EST
Generative AI Explorer - Agent Platform Earned жовт. 19, 2023 EDT
Create Image Captioning Models Earned черв. 19, 2023 EDT
Encoder-Decoder Architecture Earned черв. 19, 2023 EDT
Introduction to Responsible AI - Українська Earned черв. 19, 2023 EDT
Introduction to Vertex AI Studio Earned черв. 13, 2023 EDT
Generative AI Fundamentals - Українська Earned черв. 6, 2023 EDT
Introduction to Image Generation Earned трав. 19, 2023 EDT
Transformer Models and BERT Model Earned трав. 10, 2023 EDT
Attention Mechanism Earned трав. 10, 2023 EDT
Introduction to Large Language Models - Українська Earned трав. 10, 2023 EDT
Introduction to Generative AI - Українська Earned трав. 10, 2023 EDT
Building No-Code AppSheet Apps Earned лют. 16, 2023 EST
NCAA® March Madness®: Bracketology with Google Cloud Earned лют. 14, 2023 EST
Perform Predictive Data Analysis in BigQuery Earned лют. 13, 2023 EST
BigQuery for Machine Learning Earned лют. 8, 2023 EST
DEPRECATED Applying BigQuery ML's Classification, Regression, and Demand Forecasting for Retail Applications Earned лют. 8, 2023 EST
Create ML Models with BigQuery ML Earned лют. 7, 2023 EST

Complete the intermediate Implement Cloud Security Fundamentals on Google Cloud skill badge course to demonstrate skills in the following: creating and assigning roles with Identity and Access Management (IAM); creating and managing service accounts; enabling private connectivity across virtual private cloud (VPC) networks; restricting application access using Identity-Aware Proxy; managing keys and encrypted data using Cloud Key Management Service (KMS); and creating a private Kubernetes cluster.

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Complete the advanced Deploy Multi-Agent Architectures skill badge to demonstrate skills in the following: building multi-agent systems with ADK, connecting agents with the Agent-to-Agent (A2A) protocol, integrating external tools using the Model Context Protocol (MCP), and deploying a complete multi-agent solution to Agent Engine. Explore other content in the Gemini Enterprise Agent Ready (GEAR) program.

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In this course, you’ll learn to use the Google Agent Development Kit to build complex, multi-agent systems. You will build agents equipped with tools, and connect them with parent-child relationships and flows to define how they interact. You’ll run your agents locally and deploy them to Agent Runtime on Gemini Enterprise Agent Platform to run as a managed agentic flow, with infrastructure decisions and resource scaling handled by Agent Runtime. Please note these labs are based off a pre-released version of this product. There may be some lag on these labs as we provide maintenance updates.

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This course covers how to use Gemini in Google Meet to enhance video collaboration. Learn to automate note-taking, utilize real-time text and speech translation, privately catch up on missed discussions, and instantly optimize your audio and video quality. Explore other content in the Gemini Enterprise Agent Ready (GEAR) program.

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This course covers how to use Gemini in Google Sheets to automate data organization and crunch complex numbers. Learn to structure tables from raw text, auto-populate data using natural language, generate native spreadsheet formulas on demand, and rapidly extract high-level insights or visualizations without needing advanced spreadsheet expertise. Explore other content in the Gemini Agent Ready (GEAR) program.

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This course covers how to use Gemini in Google Docs to streamline your writing and content creation. Learn to draft documents from scratch, refine writing tone and clarity with interactive tools, summarize complex files, and seamlessly transform long-form text into structured assets for other Google Workspace applications. Explore other content in the Gemini Enterprise Agent Ready (GEAR) program.

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This course covers how to use Gemini in Google Meet to enhance video collaboration. Learn to automate note-taking, utilize real-time text and speech translation, privately catch up on missed discussions, and instantly optimize your audio and video quality. Explore other content in the Gemini Enterprise Agent Ready (GEAR) program.

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This course covers how to use Gemini in Google Slides to transform concepts into visually compelling presentations. Learn to generate structured decks from a single prompt or existing source document, refine slide layouts using automated design enhancement, create custom vector graphics and photos, and dynamically craft on-slide copy alongside precise speaker notes.

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The Generative AI Explorer - Agent Platform course 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 Gemini models in Agent Platform. 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 custom training in Agent Platform and deploy it to an endpoint in Agent Platform.

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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 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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Це ознайомлювальний курс мікронавчання, який має пояснити, що таке відповідальне використання штучного інтелекту, чому воно важливе і як компанія Google реалізує його у своїх продуктах. Крім того, у цьому курсі викладено 7 принципів Google щодо штучного інтелекту.

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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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Щоб отримати кваліфікаційний значок, пройдіть курси "Introduction to Generative AI", "Introduction to Large Language Models" й "Introduction to Responsible AI". Пройшовши завершальний тест, ви підтвердите, що засвоїли основні поняття, які стосуються генеративного штучного інтелекту. Кваліфікаційний значок – це цифровий значок від платформи Google Cloud, який свідчить, що ви знаєтеся на продуктах і сервісах Google Cloud. Щоб опублікувати кваліфікаційний значок, зробіть свій профіль загальнодоступним, а також додайте значок у профіль у соціальних мережах.

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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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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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У цьому ознайомлювальному курсі мікронавчання ви дізнаєтеся, що таке великі мовні моделі, де вони використовуються і як підвищити їх ефективність коригуванням запитів. Він також охоплює інструменти Google, які допоможуть вам створювати власні додатки на основі генеративного штучного інтелекту.

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Це ознайомлювальний курс мікронавчання, який має пояснити, що таке генеративний штучний інтелект, як він використовується й чим відрізняється від традиційних методів машинного навчання. Він також охоплює інструменти Google, які допоможуть вам створювати власні додатки на основі генеративного штучногоінтелекту.

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This course introduces you to the fundamentals of no-code application development and the capabilities offered by Google Cloud's AppSheet. AppSheet helps in digitizing and automating manual or paper-based business processes to turn them into mobile and web apps.

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In this series of labs you will learn how to use BigQuery to analyze NCAA basketball data with SQL. Build a Machine Learning Model to predict the outcomes of NCAA March Madness basketball tournament games.

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Complete the intermediate Perform Predictive Data Analysis in BigQuery skill badge course to demonstrate skills in the following: creating datasets in BigQuery by importing CSV and JSON files; harnessing the power of BigQuery with sophisticated SQL analytical concepts, including using BigQuery ML to train an expected goals model on soccer event data and evaluate the impressiveness of World Cup goals.

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Want to build ML models in minutes instead of hours using just SQL? BigQuery ML democratizes machine learning by letting data analysts create, train, evaluate, and predict with machine learning models using existing SQL tools and skills. In this series of labs, you will experiment with different model types and learn what makes a good model.

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In this course you will learn how to use several BigQuery ML features to improve retail use cases. Predict the demand for bike rentals in NYC with demand forecasting, and see how to use BigQuery ML for a classification task that predicts the likelihood of a website visitor making a purchase.

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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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