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Gabriel Cruz

Member since 2024

Diamond League

28525 points
Badge for Preparing for Your Google Workspace Administrator Journey Preparing for Your Google Workspace Administrator Journey Earned Dec 13, 2024 EST
Badge for Working with Notebooks in Vertex AI Working with Notebooks in Vertex AI Earned Nov 19, 2024 EST
Badge for Introduction to AI and Machine Learning on Google Cloud Introduction to AI and Machine Learning on Google Cloud Earned Nov 19, 2024 EST
Badge for Professional Machine Learning Engineer Study Guide Professional Machine Learning Engineer Study Guide Earned Nov 19, 2024 EST
Badge for Gemini in Google Meet Gemini in Google Meet Earned Nov 13, 2024 EST
Badge for Gemini in Gmail Gemini in Gmail Earned Nov 13, 2024 EST
Badge for Introduction to Gemini for Google Workspace Introduction to Gemini for Google Workspace Earned Nov 13, 2024 EST
Badge for Responsible AI for Developers: Fairness & Bias Responsible AI for Developers: Fairness & Bias Earned Nov 13, 2024 EST
Badge for Responsible AI for Developers: Privacy & Safety Responsible AI for Developers: Privacy & Safety Earned Nov 13, 2024 EST
Badge for Machine Learning Operations (MLOps) for Generative AI Machine Learning Operations (MLOps) for Generative AI Earned Nov 12, 2024 EST
Badge for Responsible AI for Developers: Interpretability & Transparency Responsible AI for Developers: Interpretability & Transparency Earned Nov 12, 2024 EST
Badge for Vector Search and Embeddings Vector Search and Embeddings Earned Nov 11, 2024 EST
Badge for Introduction to Vertex AI Studio Introduction to Vertex AI Studio Earned Nov 11, 2024 EST
Badge for Create Image Captioning Models Create Image Captioning Models Earned Nov 11, 2024 EST
Badge for Transformer Models and BERT Model Transformer Models and BERT Model Earned Nov 11, 2024 EST
Badge for Inspect Rich Documents with Gemini Multimodality and Multimodal RAG Inspect Rich Documents with Gemini Multimodality and Multimodal RAG Earned Nov 8, 2024 EST
Badge for Encoder-Decoder Architecture Encoder-Decoder Architecture Earned Nov 8, 2024 EST
Badge for Attention Mechanism Attention Mechanism Earned Nov 7, 2024 EST
Badge for Introduction to Image Generation Introduction to Image Generation Earned Nov 7, 2024 EST
Badge for Responsible AI: Applying AI Principles with Google Cloud Responsible AI: Applying AI Principles with Google Cloud Earned Nov 7, 2024 EST
Badge for Prompt Design in Vertex AI Prompt Design in Vertex AI Earned Nov 7, 2024 EST
Badge for Build a Secure Google Cloud Network Build a Secure Google Cloud Network Earned Nov 7, 2024 EST
Badge for Prepare Data for ML APIs on Google Cloud Prepare Data for ML APIs on Google Cloud Earned Nov 7, 2024 EST
Badge for Set Up an App Dev Environment on Google Cloud Set Up an App Dev Environment on Google Cloud Earned Nov 6, 2024 EST
Badge for Implement Load Balancing on Compute Engine Implement Load Balancing on Compute Engine Earned Nov 6, 2024 EST
Badge for Google Cloud Computing Foundations: Data, ML, and AI in Google Cloud Google Cloud Computing Foundations: Data, ML, and AI in Google Cloud Earned Nov 6, 2024 EST
Badge for Google Cloud Computing Foundations: Networking & Security in Google Cloud Google Cloud Computing Foundations: Networking & Security in Google Cloud Earned Oct 29, 2024 EDT
Badge for Google Cloud Computing Foundations: Infrastructure in Google Cloud Google Cloud Computing Foundations: Infrastructure in Google Cloud Earned Oct 25, 2024 EDT
Badge for Google Cloud Computing Foundations: Cloud Computing Fundamentals Google Cloud Computing Foundations: Cloud Computing Fundamentals Earned Oct 24, 2024 EDT
Badge for Introduction to Responsible AI Introduction to Responsible AI Earned Oct 17, 2024 EDT
Badge for Introduction to Large Language Models Introduction to Large Language Models Earned Oct 17, 2024 EDT
Badge for Introduction to Generative AI Introduction to Generative AI Earned Oct 11, 2024 EDT

This course helps learners prepare to study for the Professional Google Workspace Administrator Certification exam. Learners will be exposed to and engage with exam topics through a series of readings, diagnostic questions, and knowledge checks. After completing this course, learners will have a personalized workbook that will guide them through the rest of their certification readiness journey.

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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 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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This course helps learners create a study plan for the PMLE (Professional Machine Learning Engineer) certification exam. Learners explore the breadth and scope of the domains covered in the exam. Learners assess their exam readiness and create their individual study plan.

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Gemini for Google Workspace is an add-on that provides users 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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Gemini for Google Workspace is an add-on that 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 is an add-on that 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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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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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 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 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 Vertex AI Vector Search and describes how it can be used to build a search application with large language model (LLM) APIs for embeddings. The course consists of conceptual lessons on vector search and text embeddings, practical demos on how to build vector search on Vertex AI, and a hands-on lab.

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This course introduces Vertex AI Studio, a tool for prototyping and customizing generative AI models. Through immersive lessons, engaging demos, and a hands-on lab, you'll explore the generative AI workflow and learn how to leverage Vertex AI Studio for Gemini multimodal applications, prompt design, and model tuning. The aim is to enable you to unlock the potentials of these models 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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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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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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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. 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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Earn a skill badge by completing the Build and Secure Networks in Google Cloud course, where you will learn about multiple networking-related resources to build, scale, and secure your applications on Google Cloud. 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 the skill badge, and final assessment challenge lab, to receive a digital badge that you can share with your network.

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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. 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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Earn a skill badge by completing the Set Up an App Dev Environment on Google Cloud course, where you learn how to build and connect storage-centric cloud infrastructure using the basic capabilities of the of the following technologies: Cloud Storage, Identity and Access Management, Cloud Functions, and Pub/Sub. 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 skill badge that you can share with your network.

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Complete the introductory Implement Load Balancing on Compute Engine skill badge to demonstrate skills in the following: writing gcloud commands and using Cloud Shell, creating and deploying virtual machines in Compute Engine, and configuring network and HTTP load balancers. 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 skill badge that you can share with your network.

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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 final course in the series reviews managed big data services, machine learning and its value, and how to demonstrate your skill set in Google Cloud further by earning Skill Badges.

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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 third course covers cloud automation and management tools and building secure networks.

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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

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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 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 7 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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