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Choukri El Ouastani

Member since 2022

Bronze League

46390 points
Build with Gemini Earned سبتمبر 29, 2026 EDT
Agentic Strategy: Discover, Design, and Prototype Earned سبتمبر 13, 2026 EDT
Liftoff with Google Antigravity: Build a Video Game with AI Earned سبتمبر 6, 2026 EDT
Govern Agent Access with Gemini platform Earned أغسطس 4, 2026 EDT
Add Agents to Gemini Earned أغسطس 4, 2026 EDT
Deploy Gemini with Workspace Data Sources and Model Armor Earned أغسطس 3, 2026 EDT
Serverless Data Processing with Dataflow: Develop Pipelines Earned يوليو 17, 2026 EDT
Find, Explore and Deploy Model Garden Models Earned يوليو 2, 2026 EDT
Evaluate and Improve Agent Development Kit Agents Earned يونيو 25, 2026 EDT
Accelerate Development with Antigravity Earned يونيو 23, 2026 EDT
Deploy an Agent with Agent Development Kit (ADK) Earned يونيو 20, 2026 EDT
Orchestrate Data Lifecycle Automation with Data Agents Earned يونيو 19, 2026 EDT
Machine Learning Operations (MLOps) with Agent Platform: Model Evaluation Earned يونيو 18, 2026 EDT
Model Armor: Securing AI Deployments Earned يونيو 5, 2026 EDT
[DEPRECATED] Evaluate ADK Agents with Vertex AI Gen AI Evaluation Service Earned مايو 20, 2026 EDT
Deploy Multi-Agent Systems with Gemini Enterprise Agent Platform Earned مايو 19, 2026 EDT
Manage Agent Memory and State Earned مايو 18, 2026 EDT
Add Agent Capabilities With Tools Earned مايو 17, 2026 EDT
Understand Google Cloud Agents Earned مايو 16, 2026 EDT
Deploy Multi-Agent Architectures Earned أبريل 4, 2026 EDT
Build Your First Agent with Agent Development Kit (ADK) Earned مارس 25, 2026 EDT
Introduction to Large Language Models Earned مارس 22, 2026 EDT
Build a Multimodal GenAI Pipeline with BigQuery Earned مارس 21, 2026 EDT
Data Warehousing for Partners: Migrate Data to BigQuery Earned مارس 19, 2026 EDT
Google Skills Arcade Base Camp January 2026 Earned يناير 28, 2026 EST
Deliver Effective Product Demos Earned ديسمبر 24, 2025 EST
Build Data Analytics Solutions on Google Cloud Earned ديسمبر 23, 2025 EST
Discover Business Value for Customers Earned ديسمبر 12, 2025 EST
Generative AI Explorer : Vertex AI Earned ديسمبر 2, 2025 EST
Evaluate Gen AI model and agent performance Earned أغسطس 15, 2025 EDT
Deploy and Evaluate Model Garden Models Earned أغسطس 14, 2025 EDT
Deploy a RAG application with vector search in Firestore Earned أغسطس 11, 2025 EDT
Implement Hybrid Search Earned أغسطس 9, 2025 EDT
Implement RAG with Gemini platform Earned أغسطس 8, 2025 EDT
Create Embeddings, Vector Search, and RAG with BigQuery Earned أغسطس 8, 2025 EDT
Work with Gemini Models in BigQuery Earned أغسطس 7, 2025 EDT
Edit images with Imagen Earned أغسطس 7, 2025 EDT
Generate and Edit Media in Agent Platform Earned أغسطس 5, 2025 EDT
Empower Gen AI Apps with Tool Use Earned يوليو 19, 2025 EDT
Extend Gemini with controlled generation and Tool use Earned يوليو 19, 2025 EDT
[DEPRECATED] Integrate Vertex AI Search and Conversation into Voice and Chat Apps Earned يونيو 4, 2025 EDT
Data Lake Modernization on Google Cloud: Cloud Composer Earned مايو 9, 2025 EDT
Create Image Captioning Models Earned أبريل 17, 2025 EDT
Introduction to Image Generation Earned أبريل 17, 2025 EDT
Transformer Models and BERT Model Earned أبريل 17, 2025 EDT
Encoder-Decoder Architecture Earned مارس 8, 2025 EST
Attention Mechanism Earned مارس 8, 2025 EST
Boost Productivity with Gemini in BigQuery Earned يناير 13, 2025 EST
Text Prompt Engineering Techniques Earned يناير 5, 2025 EST
[DEPRECATED] Generative AI Fundamentals Earned يناير 5, 2025 EST
Monitor and Manage Data in BigQuery Earned ديسمبر 31, 2024 EST
Introduction to Vertex AI Studio Earned ديسمبر 30, 2024 EST
Migrate Cloudera to Google Cloud Earned ديسمبر 29, 2024 EST
Responsible AI: Applying AI Principles with Google Cloud Earned ديسمبر 29, 2024 EST
[DEPRECATED]Introduction to Responsible AI Earned ديسمبر 29, 2024 EST
[DEPRECATED] Generative AI for Business Leaders Earned ديسمبر 29, 2024 EST
Build a Data Mesh with Knowledge Catalog Earned أكتوبر 22, 2024 EDT
Derive Insights from BigQuery Data Earned أكتوبر 20, 2024 EDT
Data Warehousing for Partners: Design in BigQuery Earned سبتمبر 27, 2024 EDT
Data Warehousing for Partners: Enable Google Cloud Customers Earned سبتمبر 24, 2024 EDT
Automate Data Migrations to BigQuery Earned أبريل 14, 2024 EDT
Implement DevOps Workflows in Google Cloud Earned مارس 31, 2024 EDT
Build a Data Warehouse with BigQuery Earned مارس 27, 2024 EDT
Developing a Google SRE Culture Earned مارس 18, 2024 EDT
Build Infrastructure with Terraform on Google Cloud Earned يناير 3, 2024 EST
Set Up an App Dev Environment on Google Cloud Earned نوفمبر 19, 2023 EST
Develop Serverless Applications on Cloud Run Earned يوليو 15, 2023 EDT
Deploy Kubernetes Applications on Google Cloud Earned يونيو 6, 2023 EDT
Build Data Lakes and Data Warehouses on Google Cloud Earned مايو 27, 2022 EDT
[DEPRECATED] Google Cloud Big Data and Machine Learning Fundamentals Earned مايو 8, 2022 EDT

This is intended for individuals who completed the Build with Gemini workshop to validate the knowledge gained in that workship and earn a Skill Badge

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Step into the future with Agentic Strategy: Discover, Design, and Prototype. This course allows you to master the Google Agentic AI Transformation Framework and move beyond chatbots to autonomous teammates. Through a retail use case, you will learn to quantify business value, prioritize AI projects, map critical user journeys, and build functional prototypes using no-code tools like Google AI Studio and Gemini Enterprise. This course provides a solid roadmap necessary for business and technical leaders to lead their organization into the agentic AI era. Explore other content in the Gemini Agent Ready (GEAR) program.

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In this course, you'll learn to build software at a propulsive speed by orchestrating agents in Google Antigravity. This hands-on course will first guide you to install and get started with Antigravity by making a Hello, World(s)! app. Next, you'll use Firebase to deploy that app. Finally, you'll build a video game, Voyager, by orchestrating agents in Antigravity to work in parallel. Explore other content in the Gemini Agent Ready (GEAR) program.

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In this challenge lab, you will act as a cloud engineer supporting the Cymbal Pools finance team. Your mission is to deploy a BigQuery-enabled agent to Agent Runtime to help process invoice data using natural language. Rather than building from scratch, you inherit an unsecured deployment. You must establish basic data governance by configuring the Agent Development Kit (ADK), deploying the agent with a dedicated SPIFFE identity, identifying permission blocks, and applying least-privilege IAM roles so the agent can safely query and update the BigQuery database from the Agent Runtime Playground.

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In this challenge lab, you will demonstrate your ability to add agents to Gemini. You will build an agent with Agent Designer, and you will build a no-code agent with Agent Development Kit, deploy it to Agent Engine, and add it to Gemini.

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In this challenge lab, you act as a Security Engineer deploying a secure Gemini environment for Cymbal Bank. You will ground Gemini in web search and internal Workspace sources to ensure accurate, contextual responses. To maintain compliance, you will configure Model Armor policies to filter sensitive data and block threats like prompt injections and malicious URLs. Finally, you will manage specific end-user features to customize the AI experience safely

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In this second installment of the Dataflow course series, we are going to be diving deeper on developing pipelines using the Beam SDK. We start with a review of Apache Beam concepts. Next, we discuss processing streaming data using windows, watermarks and triggers. We then cover options for sources and sinks in your pipelines, schemas to express your structured data, and how to do stateful transformations using State and Timer APIs. We move onto reviewing best practices that help maximize your pipeline performance. Towards the end of the course, we introduce SQL and Dataframes to represent your business logic in Beam and how to iteratively develop pipelines using Beam notebooks.

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Model Garden on Gemini platform is a model library that helps you discover, test, and deploy models from Google and Google partners. Learn how to explore the available models and select the right ones for your use case. And how to deploy and interact with Model Garden models through the Google Cloud console and APIs.

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Complete the Evaluate and Improve Agent Development Kit Agents skill badge to demonstrate your ability to use ADK's evaluation tools to "hill climb" — making measurable, iterative improvements to an agent. You will run an initial evaluation to establish a baseline, apply optimization techniques, and re-evaluate the agent to measure your success.

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Complete the Accelerate Development with Antigravity skill badge to demonstrate your proficiency in using the Antigravity IDE for developing agentic workflows. You will be tasked with configuring an MCP server, authoring custom agent skills and rules, prototyping with the Agents CLI, and deploying to the Google Cloud Agent Runtime. 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 assessment challenge lab, to receive a skill badge that you can share with your network. When you complete this course, you can earn the badge displayed here and claim it on Credly! Boost your cloud career by showing the world the skills you have developed!

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In this challenge lab, you will demonstrate your ability to author agents using Agent Development Kit (ADK), deploy those agents to Agent Engine, and use them from a web app. Complete the challenge lab to earn a Google Cloud skill badge.

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Complete the Orchestrate Data Lifecycle Automation with Data Agents skill badge to demonstrate your proficiency in modernizing data infrastructure using AI-driven automation. Emphasis is placed on acting as an 'Agent Orchestrator'—leveraging specialized Data Agents to build resilient Dataform pipelines, enforce Dataplex governance, and accelerate data science workflows in Colab Enterprise. 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 assessment challenge lab to receive a skill badge that you can share with your network. When you complete this course, you can earn the badge displayed here and claim it on Credly! Boost your cloud career by showing the world the skills you have developed!

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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 Agent Platform, participants will learn how to implement robust evaluation processes for model selection, optimization, and continuous monitoring.

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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 apps. Explore other content in the Gemini Agent Ready (GEAR) program.

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Evaluation is important at every step of your Gen AI development process. In this course you will learn how to evaluate gen AI agents built using agent frameworks.

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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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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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This course provides an overview of building AI Agents on Google Cloud. You discover how to boost employee productivity with Gemini and craft dynamic customer experiences using Conversational Agents and Customer Experience (CX) Studio. You learn how to leverage Gemini alongside the Agent Development Kit (ADK) to design custom, multi-agent architectures. You also explore choosing the right tool for each use case, grounding models in enterprise data, and deploying agents securely into production.

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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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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 Gemini platform. 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 Explore other content in the Gemini Agent Ready (GEAR) program.

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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 Agent Ready (GEAR) program.

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Complete the Build a "Data to AI" Pipeline with BigQuery and Gemini skill badge to demonstrate your proficiency in modernizing legacy product catalogs through end-to-end data engineering and AI orchestration. Emphasis is placed on unifying structured and unstructured data using BigLake, leveraging Gemini for multimodal processing, and deploying scalable machine learning solutions via Vertex AI. 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 assessment challenge lab to receive a skill badge that you can share with your network. When you complete this course, you can earn the badge displayed here and claim it on Credly! Boost your cloud career by showing the world the skills you have developed!

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This course identifies best practices for migrating data warehouses to BigQuery and the key skills required to perform successful migration.

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Welcome to Base Camp, where you’ll develop key Google Cloud skills (available in Spanish and Portuguese too!) and earn an exclusive credential that will open doors to the cloud for you. No prior experience is required!

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This course is designed to enhance your demonstration skills and help you deliver impactful presentations.

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This Data Analytics course consists of a series of advanced-level labs designed to validate your proficiency in using Google Cloud services. Each lab presents a set of the required tasks that you must complete with minimal assistance.

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The course aims to train Google technical sales partners on the business value discovery process using proprietary content.

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This content is deprecated. Please see the latest version of the course, here.

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Complete the Evaluate Gen AI model and agent performance skill badge to demonstrate your ability to use the Gen AI evaluation service. You will evaluate models to select the best model for a given task, compare models against each other and evaluate the performance of agents. 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 assessment challenge lab, to receive a skill badge that you can share with your network. When you complete this course, you can earn the badge displayed here and claim it on Credly! Boost your cloud career by showing the world the skills you have developed!

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In this skill bagde, you will demonstrate your ability to use and compare models available in the Vertex AI Model Garden. You'll deploy a model to a Vertex AI Endpoint, query other models via their API, and use Vertex AI's Gen AI evaluation service to measure the performance of multiple models.

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This lab tests your ability to develop a real-world Generative AI Q&A solution using a RAG framework. You will use Firestore as a vector database and deploy a Flask app as a user interface to query a food safety knowledge base.

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Learn how to create Hybrid Search applications using Vector Search on Gemini platform to combine semantic searching with keyword search to return results based on both semantic meaning and keyword matching.

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Learn how to build your own Retrieval-Augmented Generation (RAG) solutions for greater control and flexibility than out-of-the-box implementations. Create a custom RAG solution using Gemini platform APIs, vector stores, and the LangChain framework.

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This course explores a Retrieval Augmented Generation (RAG) solution in BigQuery to mitigate AI hallucinations. It introduces a RAG workflow that encompasses creating embeddings, searching a vector space, and generating improved answers. The course explains the conceptual reasons behind these steps and their practical implementation with BigQuery. By the end of the course, learners will be able to build a RAG pipeline using BigQuery and generative AI models like Gemini and embedding models to address their own AI hallucination use cases.

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This course demonstrates how to use AI/ML models for generative AI tasks in BigQuery. Through a practical use case involving customer relationship management, you learn the workflow of solving a business problem with Gemini models. To facilitate comprehension, the course also provides step-by-step guidance through coding solutions using both SQL queries and Python notebooks.

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Complete the Edit images with Imagen skill badge to demonstrate your skills with Imagen's mask modes and editing modes to edit images according to certain prompts. 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 assessment challenge lab, to receive a skill badge that you can share with your network. When you complete this course, you can earn the badge displayed here and claim it on Credly! Boost your cloud career by showing the world the skills you have developed!

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This course dives into the world of media creation with Gemini Enterprise Agent Platform using Nano Banana and Veo. Learn to design text and image-based prompts to produce high-quality, consistent images, and captivating, cinematic video clips. You'll also learn to refine generated assets using core editing functions. Finally, this course guides you through multi-tool workflow implementations for creative control and consistency, empowering you to transform images into video clips and leverage Gemini for prompt writing assistance and feedback.

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An LLM-based application can process language in a way that resembles thought. But if you want to extend its capabilities to take actions by running other functions you have coded, you will need to use function calling. This can also be referred to as tool use. Additionally, you can give a model the ability to search Google or search a data store of documents to ground its responses. In other words, to base its answers on that information. In this course, you’ll explore these concepts.

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Complete the Extend Gemini with controlled generation and Tool use skill badge to demonstrate your proficiency in connecting models to external tools and APIs. This allows models to augment their knowledge, extend their capabilities and interact with external systems to take actions such as sending an email. 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 assessment challenge lab, to receive a skill badge that you can share with your network. When you complete this course, you can earn the badge displayed here and claim it on Credly! Boost your cloud career by showing the world the skills you have developed!"

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This course on Integrate Vertex AI Search and Conversation into Voice and Chat Apps is composed of a set of labs to give you a hands on experience to interacting with new Generative AI technologies. You will learn how to create end-to-end search and conversational experiences by following examples. These technologies complement predefined intent-based chat experiences created in Dialogflow with LLM-based, generative answers that can be based on your own data. Also, they allow you to porvide enterprise-grade search experiences for internal and external websites to search documents, structure data and public websites.

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Welcome to Cloud Composer, where we discuss how to orchestrate data lake workflows with Cloud Composer.

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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 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 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 explores Gemini in BigQuery, a suite of AI-driven features to assist data-to-AI workflow. These features include data exploration and preparation, code generation and troubleshooting, and workflow discovery and visualization. Through conceptual explanations, a practical use case, and hands-on labs, the course empowers data practitioners to boost their productivity and expedite the development pipeline.

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Text Prompt Engineering Techniques introduces you to consider different strategic approaches & techniques to deploy when writing prompts for text-based generative AI tasks.

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Earn a skill badge by passing the final quiz, you'll demonstrate your understanding of foundational concepts in generative AI. A skill badge is a digital badge issued by Google Cloud in recognition of your knowledge of Google Cloud products and services. Share your skill badge by making your profile public and adding it to your social media profile.

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This skill badge aims to evaluate a partner's ability to utilize BigQuery's features and capabilities to manage and analyze large datasets. Learners will gain hands-on experience through labs and achieve solid understanding of BigQuery's foundational concepts and features.

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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 skill badge aims to evaluate a partner's ability to migrate data from Cloudera to Google Cloud Platform. Learners will gain hands-on experience through labs and achieve comprehensive knowledge and practical skills for migrating data from Cloudera to Google Cloud Platform.

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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 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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A Business Leader in Generative AI can articulate the capabilities of core cloud Generative AI products and services and understand how they benefit organizations. This course provides an overview of the types of opportunities and challenges that companies often encounter in their digital transformation journey and how they can leverage Google Cloud's generative AI products to overcome these challenges.

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Complete the introductory Build a Data Mesh with Knowledge Catalog skill badge to demonstrate skills in the following: building a data mesh with Knowledge Catalog to facilitate data security, governance, and discovery on Google Cloud. You practice and test your skills in tagging assets, assigning IAM roles, and assessing data quality in Knowledge Catalog.

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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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Welcome to Design in BigQuery, where we map Enterprise Data Warehouse concepts and components to BigQuery and Google data services with a focus on schema design.

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This course discusses the key elements of Google's Data Warehouse solution portfolio and strategy.

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This skill badge aims to provide partners an introduction to BigQuery Data Transfer Service and Migration Service, two powerful tools for managing and migrating data in the cloud. Learners will learn how to leverage these tools to efficiently migrate and manage data, and gain hands-on experience through labs.

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Complete the intermediate Implement DevOps Workflows in Google Cloud skill badge to demonstrate skills in the following: creating and managing Git repositories, launching, managing, and scaling deployments on Google Kubernetes Engine (GKE), and architecting CI/CD pipelines that automate container image builds and deployments to GKE.

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Complete the intermediate Build a Data Warehouse with BigQuery skill badge course to demonstrate skills in the following: joining data to create new tables, troubleshooting joins, appending data with unions, creating date-partitioned tables, and working with JSON, arrays, and structs in BigQuery.

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In many IT organizations, incentives are not aligned between developers, who strive for agility, and operators, who focus on stability. Site reliability engineering, or SRE, is how Google aligns incentives between development and operations and does mission-critical production support. Adoption of SRE cultural and technical practices can help improve collaboration between the business and IT. This course introduces key practices of Google SRE and the important role IT and business leaders play in the success of SRE organizational adoption.

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Complete the intermediate Build Infrastructure with Terraform on Google Cloud skill badge to demonstrate skills in the following: Infrastructure as Code (IaC) principles using Terraform, provisioning and managing Google Cloud resources with Terraform configurations, effective state management (local and remote), and modularizing Terraform code for reusability and organization.

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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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Complete the intermediate Develop Serverless Applications on Cloud Run skill badge course to demonstrate skills in the following: integrating Cloud Run with Cloud Storage for data management, architecting resilient asynchronous systems using Cloud Run and Pub/Sub, constructing REST API gateways powered by Cloud Run, and building and deploying services on Cloud Run.

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Complete the intermediate Deploy Kubernetes Applications on Google Cloud skill badge course to demonstrate skills in the following: Configuring and building Docker container images.Creating and managing Google Kubernetes Engine (GKE) clusters.Utilizing kubectl for efficient cluster management.Deploying Kubernetes applications with robust continuous delivery (CD) practices.

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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 introduces the Google Cloud big data and machine learning products and services that support the data-to-AI lifecycle. It explores the processes, challenges, and benefits of building a big data pipeline and machine learning models with Vertex AI on Google Cloud.

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