Yumi HEO
Member since 2024
Diamond League
5525 points
Secure Lakehouse Data
Earned Jul 28, 2026 EDT
Mitigate Threats and Vulnerabilities with Security Command Center
Earned May 17, 2026 EDT
Implement Cloud Collaboration and Productivity Workflows
Earned May 17, 2026 EDT
Optimize Costs for Google Kubernetes Engine
Earned May 16, 2026 EDT
The Basics of Google Cloud Compute
Earned May 16, 2026 EDT
Use APIs to Work with Cloud Storage
Earned May 16, 2026 EDT
Monitor and Manage Google Cloud Resources
Earned May 16, 2026 EDT
Responsible AI for Developers: Privacy & Safety
Earned May 16, 2026 EDT
Machine Learning Operations (MLOps) for Generative AI
Earned May 15, 2026 EDT
Google DeepMind: 03 Design And Train Neural Networks
Earned May 15, 2026 EDT
Encoder-Decoder Architecture
Earned May 15, 2026 EDT
Transformer Models and BERT Model
Earned May 15, 2026 EDT
Attention Mechanism
Earned May 15, 2026 EDT
Share Data Using Google Data Cloud
Earned May 14, 2026 EDT
Derive Insights from BigQuery Data
Earned May 14, 2026 EDT
Monitoring in Google Cloud
Earned May 13, 2026 EDT
Discover and Protect Sensitive Data Across Your Ecosystem
Earned May 13, 2026 EDT
Analyze and Reason on Multimodal Data with Gemini
Earned May 12, 2026 EDT
Analyze Images with the Cloud Vision API
Earned May 10, 2026 EDT
Using the Google Cloud Speech API
Earned May 10, 2026 EDT
Responsible AI for Developers: Interpretability & Transparency
Earned May 10, 2026 EDT
Prepare Data for ML APIs on Google Cloud
Earned May 9, 2026 EDT
Implementing Cloud Load Balancing for Compute Engine
Earned May 9, 2026 EDT
Innovating with Google Cloud Artificial Intelligence
Earned Apr 27, 2026 EDT
Analyze Sentiment with Natural Language API
Earned Apr 27, 2026 EDT
Google Cloud AI and ML Solutions for the Public Sector
Earned Apr 26, 2026 EDT
Responsible AI for Developers: Fairness & Bias
Earned Apr 26, 2026 EDT
Responsible AI: Applying AI Principles with Google Cloud
Earned Apr 26, 2026 EDT
Introduction to AI and Machine Learning on Google Cloud
Earned Apr 22, 2026 EDT
[DEPRECATED]Introduction to Responsible AI
Earned Apr 15, 2026 EDT
Introduction to Image Generation
Earned Apr 15, 2026 EDT
Introduction to Large Language Models
Earned Apr 14, 2026 EDT
Introduction to Generative AI
Earned Apr 11, 2026 EDT
Complete the introductory Secure Lakehouse Data skill badge course to demonstrate skills with IAM, BigQuery, Lakehouse, and Knowledge Catalog to create and secure Lakehouse tables.
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Complete the intermediate Mitigate Threats and Vulnerabilities with Security Command Center skill badge course to demonstrate skills in the following: preventing and managing environment threats, identifying and mitigating application vulnerabilities, and responding to security anomalies.
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Earn an introductory skill badge by completing the Implement Cloud Collaboration and Productivity Workflows course, where you will get introduced to Google's collaborative platform and learn to use Gmail, Calendar, Meet, Drive, Sheets, and AppSheet.
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Complete the intermediate Optimize Costs for Google Kubernetes Engine skill badge course to demonstrate skills in the following: creating and managing multi-tenant clusters, monitoring resource usage by namespace, configuring cluster and pod autoscaling for efficiency, setting up load balancing for optimal resource distribution, and implementing liveness and readiness probes to ensure application health and cost-effectiveness.
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Earn a skill badge by completing the The Basics of Google Cloud Compute skill badge course, where you learn how to work with virtual machines (VMs), persistent disks, and web servers using Compute Engine.
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Complete the introductory Use APIs to Work with Cloud Storage skill badge to demonstrate skills in the following: using APIs to work with Cloud Storage resources, including the Cloud Storage API.
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Complete the introductory Monitor and Manage Google Cloud Resources skill badge to demonstrate skills in the following: granting and revoking IAM permissions; installing monitoring and logging agents; creating, deploying, and testing an event-driven Cloud Run function.
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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 Gemini Enterprise Agent Platform empowers AI teams to streamline MLOps processes and achieve success in Generative AI projects.
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In this Google DeepMind course you will focus on the training process for machine learning models. You will learn how to spot and mitigate issues when training a model, such as overfitting and underfitting. In practical coding labs, you will implement and evaluate the multilayer perceptron for simple classification tasks. This will provide insights into the mechanics of training a neural network model and the backpropagation algorithm. Research case studies will demonstrate how neural networks power real-world models. Additionally, you will consider the broader social impacts of innovation by looking beyond immediate benefits to anticipate potential risks, safety concerns, and further-reaching societal consequences.
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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 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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Earn a skill badge by completing the Share Data Using Google Data Cloud skill badge course, where you will gain practical experience with Google Cloud Data Sharing Partners, which have proprietary datasets that customers can use for their analytics use cases. Customers subscribe to this data, query it within their own platform, then augment it with their own datasets and use their visualization tools for their customer facing dashboards.
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Complete the introductory Derive Insights from BigQuery Data skill badge course to demonstrate skills in the following: Write SQL queries.Query public tables.Load sample data into BigQuery.Troubleshoot common syntax errors with the query validator in BigQuery.Create reports in Data Studio by connecting to BigQuery data.
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Complete the introductory Monitoring in Google Cloud skill badge course to demonstrate skills in the following: using Cloud Monitoring tools to monitor resources on Google Cloud.
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Complete the intermediate Discover and Protect Sensitive Data Across Your Ecosystem skill badge to demonstrate skills in the following: using Sensitive Data Protection services (including the Cloud Data Loss Prevention (DLP) API) to discover, inspect, redact, and de-identify sensitive data across your Google Cloud ecosystem including Cloud Storage files, BigQuery data, and Gen AI model responses.
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Complete the intermediate Analyze and Reason on Multimodal Data with Gemini skill badge course to demonstrate skills in the following: analyzing text, image, audio, and video data using Gemini, and reasoning about this combined information to draw conclusions and extract insights.
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Earn a skill badge by completing the Analyze Images with the Cloud Vision API quest, where you discover how to leverage the Cloud Vision API for various tasks, including extracting text from images.
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Earn a skill badge by completing the Using the Google Cloud Speech API skill badge course, where you learn how create a Speech-to-Text API request, transcribe audio speech to text, and transcribe speech.
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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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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 Managed Service for Apache Spark, and calling ML APIs including the Cloud Natural Language API, Google Cloud Speech-to-Text API, and Video Intelligence API.
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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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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.
As 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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Earn a skill badge by completing the Analyze Sentiment with Natural Language API quest, where you learn how the API derives sentiment from text.
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Building a Conversational Interface with Dialogflow CX
Building conversational interfaces will enable you to engage your constituents in delightful new ways. This section will provide an overview of Dialogflow CX, which provides a range of new capabilities, languages, and functions.
Scaling Equity and Access with Enterprise Translation Hub
Accurate, timely and cost effective document translation has long been a challenge to providing equitable and accessible service to all constituents. In this section, explore Google's Enterprise Translation solutions which provide scalable document translation leveraging best in class Artificial Intelligence and Machine Learning, while retaining the critical human in the loop for final post editing review.
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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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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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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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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 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 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. Explore other content in the Gemini Enterprise Agent Ready (GEAR) program.
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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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