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Sirisha Kagitapilli

Учасник із 2022

Діамантова ліга

Кількість балів: 45998
AppSheet Team Hierarchy Earned лип. 29, 2026 EDT
Deploy Gemini to Transform Enterprises Earned черв. 16, 2026 EDT
Add Agents to Gemini Earned черв. 16, 2026 EDT
Deploy Gemini with Workspace Data Sources and Model Armor Earned черв. 15, 2026 EDT
Use a Third-Party Identity Provider with Workforce Identity Federation Earned черв. 15, 2026 EDT
Accelerate Knowledge Exchange with Gemini Enterprise Earned черв. 15, 2026 EDT
Unlock Insights with Gemini Notebook Earned черв. 12, 2026 EDT
Model Armor: Securing AI Deployments Earned черв. 12, 2026 EDT
Agent Search Analytics on Gemini platform Earned квіт. 14, 2026 EDT
Plan Change Management for Gemini Deployments Earned квіт. 14, 2026 EDT
Agent Search UI configurations on Gemini platform Earned квіт. 13, 2026 EDT
Improve Agent Search findings on Gemini platform Earned квіт. 13, 2026 EDT
[DEPRECATED] Agent Summarization (Custom) Earned квіт. 13, 2026 EDT
Gen AI Agents: Transform Your Organization Earned лип. 2, 2025 EDT
Gen AI Apps: Transform Your Work Earned лип. 2, 2025 EDT
Gen AI: Navigate the Landscape Earned лип. 2, 2025 EDT
Gen AI: Unlock Foundational Concepts Earned лип. 2, 2025 EDT
Gen AI: Beyond the Chatbot Earned лип. 2, 2025 EDT
Build and Deploy a Generative AI solution using a RAG framework Earned груд. 31, 2024 EST
Develop Agent Search and Conversation Applications Earned груд. 26, 2024 EST
Build deterministic Virtual Agent enhanced with data stores Earned груд. 26, 2024 EST
[DEPRECATED] Integrate Vertex AI Search and Conversation into Voice and Chat Apps Earned груд. 26, 2024 EST
Text Prompt Engineering Techniques Earned груд. 23, 2024 EST
Snowflake to BigQuery Migration Earned груд. 4, 2024 EST
Azure Virtual Machines to Compute Engine Earned бер. 21, 2024 EDT
Modernization of Applications using Openshift on Google Cloud Earned бер. 21, 2024 EDT
Migrating Pivotal Cloud Foundry (PCF) to Google Cloud Earned бер. 21, 2024 EDT
[DEPRECATED] Google Security Operations - SOAR Developer Earned бер. 21, 2024 EDT
Teradata to BigQuery Earned бер. 19, 2024 EDT
BI Reporting: Looker Visualization on BigQuery Earned бер. 19, 2024 EDT
Document AI: Building a Custom Document Extractor Earned бер. 19, 2024 EDT
Migrating Hadoop Workloads Earned бер. 18, 2024 EDT
Certification Learning Path: Professional Cloud Network Engineer Earned бер. 14, 2024 EDT
Certification Learning Path: Professional Cloud DevOps Engineer Earned бер. 14, 2024 EDT
[DEPRECATED] Generative AI Fundamentals Earned бер. 14, 2024 EDT
Extends to Keep LookML DRY Earned бер. 13, 2024 EDT
Essential Cloud Infrastructure: Core Services Earned бер. 13, 2024 EDT
Essential Cloud Infrastructure: Foundation Earned бер. 13, 2024 EDT
Elastic Cloud Infrastructure: Scaling and Automation Earned бер. 13, 2024 EDT
Introduction to Vertex AI Studio Earned бер. 13, 2024 EDT
[DEPRECATED] Generative AI for Business Leaders Earned бер. 8, 2024 EST
Gemini in Google Meet Earned бер. 8, 2024 EST
Gemini in Google Docs Earned бер. 8, 2024 EST
Gemini in Google Slides Earned бер. 7, 2024 EST
Gemini in Google Sheets Earned бер. 7, 2024 EST
Gemini in Gmail Earned бер. 7, 2024 EST
Google Workspace with Gemini: Foundations of Your AI Workflow Earned бер. 7, 2024 EST
Trust and Security with Google Cloud Earned бер. 5, 2024 EST
Modernize Infrastructure and Applications with Google Cloud Earned бер. 5, 2024 EST
Exploring Data Transformation with Google Cloud Earned бер. 5, 2024 EST
Innovating with Google Cloud Artificial Intelligence Earned бер. 5, 2024 EST
Build a Certification Study Guide: PDE Exam Prep Earned лют. 28, 2024 EST
Responsible AI: Applying AI Principles with Google Cloud - Yкраїнська Earned лют. 28, 2024 EST
Introduction to Generative AI - Українська Earned лют. 20, 2024 EST
Level 3: GenAIus Conversations Earned лют. 20, 2024 EST
Encoder-Decoder Architecture Earned черв. 20, 2023 EDT
Attention Mechanism Earned черв. 20, 2023 EDT
Introduction to Image Generation Earned черв. 20, 2023 EDT
Generative AI Fundamentals - Українська Earned черв. 19, 2023 EDT
Introduction to Responsible AI - Українська Earned черв. 19, 2023 EDT
Introduction to Large Language Models - Українська Earned черв. 19, 2023 EDT
Introduction to Generative AI - Українська Earned черв. 19, 2023 EDT
Scaling with Google Cloud Operations Earned квіт. 20, 2023 EDT
Digital Transformation with Google Cloud Earned квіт. 20, 2023 EDT
Build a Certification Study Guide: ACE Exam Prep Earned квіт. 20, 2023 EDT
Підготовка даних для інтерфейсів API машинного навчання в Google Cloud Earned трав. 19, 2022 EDT
Build Data Lakes and Data Warehouses on Google Cloud Earned трав. 12, 2022 EDT
Build Streaming Data Pipelines on Google Cloud Earned трав. 10, 2022 EDT
Engineer Data for Predictive Modeling with BigQuery ML Earned трав. 9, 2022 EDT
Smart Analytics, Machine Learning, and AI on Google Cloud Earned трав. 4, 2022 EDT
Налаштування Cloud Load Balancing для Compute Engine Earned квіт. 27, 2022 EDT
Google Cloud Big Data and Machine Learning Fundamentals - українська Earned квіт. 5, 2022 EDT

A short course on defining and managing teams and organizations in Google AppSheet.

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This course covers the technical side of deploying Gemini. It discusses the overall architecture of Gemini, decisions to be made when provisioning the app infrastructure, and integrating enterprise data via Data Stores, Connectors. and Actions. The course also explores the kinds of agents that can be added to Gemini. It offers guidance towards establishing a security perimeter using IAM, VPC Service Control, Context-Aware Access, and semantic controls deployed with Model Armor in Gemini platform. Finally, it empowers administrators to fine-tune the end-user experience through comprehensive configuration options, and explains how administrators keep AI deployments secure, compliant, and performant through OpenTelemetry instrumentation and prompt logging.

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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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This course provides learners the knowledge to configure Workforce Identity Federation to grant Google Cloud and Gemini access to users that authenticate using a third-party Identity Provider. The curriculum includes theory and demo videos covering workforce identity pools, OIDC and SAML providers, attribute mapping, IAM policies, and troubleshooting guidance.

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Unite Google’s expertise in search and AI with Gemini Enterprise, a powerful tool designed to help employees find specific information from document storage, email, chats, ticketing systems, and other data sources, all from a single search bar. The Gemini Enterprise assistant can also help brainstorm, research, outline documents, and take actions like inviting coworkers to a calendar event to accelerate knowledge work and collaboration of all kinds. (Please note Gemini Enterprise was previously named Google Agentspace, there may be references to the previous product name in this course.)

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In this course, you will learn how to centralize diverse sources like PDFs, web pages, and even audio files into a single, intelligent workspace. You will learn to chat with your documents to find specific information, generate instant summaries, and verify answers with AI-powered citations.

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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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AI Applications provides built-in analytics for your Agent Search and Gemini. Learn what metrics are tracked and how to view them in this course.

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This course will guide you through designing customer engagements that result in successful, well-utilized Gemini deployments. Study the art of change management, how to identify and engage sponsors, recruit early adopters, help your customer identify and address cultural change challenges and skills gaps, and effectively deliver project communications. Additionally, learn how to support your customer to plan, offer, conduct, and evaluate end-user training, leverage partner resources, and create essential project documents.

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Initial deployment of Agent Search and Gemini takes only a few clicks, but getting the configurations right can elevate a deployment from a basic off-the-shelf app to an excellent custom search or recommendations experience. In this course, you'll learn more about the many ways you can customize and improve search, recommendations, and Gemini.

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This course covers techniques for boosting and filtering search results, as well as the implementation of meta tags for advanced search control.

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In this course you will learn how Conversational AI Agent Assist can help distill complex customer interactions into concise and clear summaries. Please note Dialogflow CX was recently renamed to Conversational Agents, Virtual agent renamed to Conversational agent, and CCAI Insights were renamed to Conversational Insights, and this course is in the process of being updated to reflect the new product names for Dialogflow CX, and Virtual Agent, CCAI Insights.

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Gen AI Agents: Transform Your Organization is the fifth and final course of the Gen AI Leader learning path. This course explores how organizations can use custom gen AI agents to help tackle specific business challenges. You gain hands-on practice building a basic gen AI agent, while exploring the components of these agents, such as models, reasoning loops, and tools. Explore other content in the Gemini Agent Ready (GEAR) program.

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Transform Your Work With Gen AI Apps is the fourth course of the Gen AI Leader learning path. This course introduces Google’s gen AI applications, such as Google Workspace with Gemini and Gemini Notebook. It guides you through concepts like grounding, retrieval augmented generation, constructing effective prompts and building automated workflows.

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Gen AI: Navigate the Landscape is the third course of the Gen AI Leader learning path. Gen AI is changing how we work and interact with the world around us. But as a leader, how can you harness its power to drive real business outcomes? In this course, you explore the different layers of building gen AI solutions, Google Cloud’s offerings, and the factors to consider when selecting a solution.

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Gen AI: Unlock Foundational Concepts is the second course of the Gen AI Leader learning path. In this course, you unlock the foundational concepts of generative AI by exploring the differences between AI, ML, and gen AI, and understanding how various data types enable generative AI to address business challenges. You also gain insights into Google Cloud strategies to address the limitations of foundation models and the key challenges for responsible and secure AI development and deployment.

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Gen AI: Beyond the Chatbot is the first course of the Gen AI Leader learning path and has no prerequisites. This course aims to move beyond the basic understanding of chatbots to explore the true potential of generative AI for your organization. You explore concepts like foundation models and prompt engineering, which are crucial for leveraging the power of gen AI. The course also guides you through important considerations you should make when developing a successful gen AI strategy for your organization.

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Demonstrate your ability to implement updated prompt engineering techniques and utilize several of Gemini's key capacilities including multimodal understanding and function calling. Then integrate generative AI into a RAG application deployed to Cloud Run. This course contains labs that are to be used as a test environment. They are deployed to test your understanding as a learner with a limited scope. These technologies can be used with fewer limitations in a real world environment.

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In this quest, you’ll use text embeddings for tasks like classification, outlier detection, text clustering and semantic search. You’ll combine semantic search with the text generation capabilities of an LLM to build Retrieval Augmented Generation (RAG) solutions, such as for question-answering systems, using Google Cloud’s Vertex AI and Google Cloud databases.

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Demonstrate the ability to create and deploy deterministic virtual agents using Dialgflow CX and augment responses by grounding results on your own data integrating with Vertex AI Agent Builder data stores and leveraging Gemini for summarizations. You will use the following technologies and Google Cloud services: Vertex AI Agent Builder Dialogflow CX Gemini

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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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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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This workload aims to upskill Google Cloud partners to perform specific tasks associated with priority workloads. Learners will perform the tasks of migrating data from Snowflake to BigQuery. Sample data will be used during the migration. Learners will complete several labs that focus on the process of transferring schema, data and related processes to corresponding Google Cloud products.There will be one or more challenge labs that will test the learners' understanding of the topics. "This learning path aims to upskill Google Cloud partners to perform specific tasks associated with priority workloads. Learners will perform the tasks of migrating data from Snowflake to BigQuery.

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Migration from Azure to Google Cloud Compute Engine using Migrate to Virtual Machines (v5) using demo VM(s). It provides a proof-of-concept that walks you through the process of replicating a VM to doing test cutover and final cutover of the VM.

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This course focuses on modernizing applications using OpenShift on Google Cloud. Throughout this course, you'll gain the skills necessary to describe and understand OpenShift and successfully re-platform it to Google Cloud.

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Welcome to the course focusing on the Migration from Pivotal Cloud Foundry to Google Cloud. This program offers a practical demonstration that guides you through the step-by-step process of transitioning applications seamlessly between these two platforms. Throughout this course, you'll engage in hands-on exercises and demos, providing a proof-of-concept journey. This course provides insights into Pivotal Cloud Foundry and Tanzu Kubernetes Grid (TKG), and their roles in cloud infrastructure. It includes hands-on sessions for installing Tanzu CLI. You'll also learn to deploy management clusters efficiently, organize cloud resources, and create workload clusters. Additionally, you will perform a workload migration from Tanzu Kubernetes Grid to Google Kubernetes Engine (GKE) and containerize an applications on Google Cloud.

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This course helps developers customize Chronicle and augment its abilities with third party integrations.

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This workload aims to upskill Google Cloud partners to perform specific tasks associated with priority workloads. Learners will perform the tasks of Migration from Teradata to BigQuery using the Data Transfer Service and the Teradata TPT Export Utility. Sample Data will be used during both methods. Learners will complete a challenge lab that focuses on the process of transferring both schema, data and SQL from a Teradata data warehouse to BigQuery.

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This workload aims to upskill Google Cloud partners to perform specific tasks for modernization using LookML on BigQuery. A proof-of-concept will take learners through the process of creating LookML visualizations on BigQuery. During this course, learners will be guided specifically on how to write Looker modeling language, also known as LookML and create semantic data models, and learn how LookML constructs SQL queries against BigQuery. At a high level, this course will focus on basic LookML to create and access BigQuery objects, and optimize BigQuery objects with LookML.

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This workload aims to upskill Google Cloud partners to perform specific tasks associated with building a Custom Doc Extractor using the Google Cloud AI solution. The following will be addressed: Service: Document AI Task: Extract fields Processors: Custom Document Extractor and Document Splitter Prediction: Using Endpoint to programmatically extract fields

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This workload aims to upskill Google Cloud partners to perform specific tasks associated with priority workloads. Learners will perform the tasks of migrating workloads from Hadoop environments to corresponding Google Cloud services and hosted products. The following will addressed will be: The Hadoop ecosystem and products Hadoop architecture and post migration architectures to Google Cloud Assessment Data transfer options Workload migrations, namely: Spark to Dataproc Serverless, Apache Oozie to Composer (Airflow), and Hive to BigQuery Security and governance Logging and Monitoring

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Good news! There’s a new updated version of this learning path available for you!Open the new Professional Cloud Network Engineer Certification Learning Path to begin, once you’ve selected the new path all your current progress will be reflected in the new version.

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Good news! There’s a new updated version of this learning path available for you!Open the new Professional Cloud DevOps Engineer Certification Learning Path to begin, once you’ve selected the new path all your current progress will be reflected in the new version.

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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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Hands on course covering the main uses of extends and the three primary LookML objects extends are used on as well as some advanced usage of extends.

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This course has been updated, please enroll in the new Essential Google Cloud Infrastructure: Core Services.

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This course has been updated, please enroll in the new Essential Google Cloud Infrastructure: Foundation.

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This course has been updated, please enroll in the new Elastic Google Cloud Infrastructure: Scaling and Automation.

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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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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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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 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 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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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 Gmail to optimize your email management. Learn to summarize lengthy threads, retrieve information using natural language, draft professional responses, and organize communication strategies directly inside your inbox. Explore other content in the Gemini Agent Ready (GEAR) program.

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Google Workspace with Gemini provides customers with generative AI features in Google Workspace. Here, you will learn about the key features of Gemini and how they can be used to improve productivity and efficiency. Continue building your skills in the full Google Workspace with Gemini learning path, and explore other content in the Gemini Agent Ready (GEAR) program.

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As organizations move their data and applications to the cloud, they must address a rapidly evolving landscape of security challenges. This course explores the foundations of cloud security, the value of Google Cloud’s secure-by-design infrastructure, and the defense-in-depth strategy, while highlighting how AI-driven operations and compliance tools help organizations meet strict global regulatory requirements. 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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Many traditional enterprises use legacy systems and apps that can't stay up-to-date with modern customer expectations. Business leaders often have to choose between maintaining their aging IT systems and investing in new products and services. This course explores these challenges and offers solutions to overcome them by using cloud technology. 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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Cloud technology is a powerful asset, and when paired with data, it becomes a catalyst for innovation and enhanced customer experiences. Exploring Data Transformation with Google Cloud examines how organizations can leverage the cloud to make their data more accessible, actionable, and valuable. 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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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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Learn how to use Gemini Notebook to create a personalized study guide for the Professional Data Engineer certification exam. You'll review Gemini Notebook features, create a notebook in Gemini Notebook, and learn how to use a study guide to practice for a certification exam.

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Що більше штучний інтелект і машинне навчання використовуються в корпоративних середовищах, то нагальнішою стає потреба розробити принципи відповідального ставлення до них. Однак говорити про принципи відповідального використання штучного інтелекту легше, ніж застосовувати їх на практиці. Цей курс допоможе вам дізнатись, як запровадити відповідальну роботу зі штучним інтелектом у вашій організації. У цьому курсі ви дізнаєтеся про підхід Google Cloud до відповідального використання ШІ, а також отримаєте практичні поради й набудете досвіду, який допоможе вам розробити власний підхід до цього завдання.

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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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Prompts are essential for guiding a Gen AI model to generate relevant and desired content. Start GenAIus conversations and earn a Google Cloud credential! No prior experience required!

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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 Gemini Enterprise Agent Platform.

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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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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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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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Organizations of all sizes are embracing the power and flexibility of the cloud to transform how they operate. However, managing and scaling cloud resources effectively can be a complex task. This course explores the fundamental concepts of modern operations, reliability, and resilience in the cloud, and how Google Cloud can help support these efforts. 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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Digital transformation is a critical journey for modern organizations, and establishing a strong baseline in cloud computing is the first step toward driving meaningful innovation. Digital Transformation with Google Cloud introduces the core technologies and strategic frameworks that help organizations modernize their operations. This course explores fundamental cloud concepts, global network infrastructure, and the shared responsibility model to help leaders navigate their path to the cloud with confidence. 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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Learn how to use Gemini Notebook to create a personalized study guide for the Associate Cloud Engineer certification exam. You'll review Gemini Notebook features, create a notebook in Gemini Notebook , and learn how to use a study guide to practice for a certification exam.

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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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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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In this course you will get hands-on in order to work through real-world challenges faced when building streaming data pipelines. The primary focus is on managing continuous, unbounded data with Google Cloud products.

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Complete the intermediate Engineer Data for Predictive Modeling with BigQuery ML skill badge to demonstrate skills in the following: building data transformation pipelines to BigQuery using Dataprep by Trifacta; using Cloud Storage, Dataflow, and BigQuery to build extract, transform, and load (ETL) workflows; and building machine learning models using BigQuery ML.

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Incorporating machine learning into data pipelines increases the ability to extract insights from data. This course covers ways machine learning can be included in data pipelines on Google Cloud. For little to no customization, this course covers AutoML. For more tailored machine learning capabilities, this course introduces Notebooks and BigQuery machine learning (BigQuery ML). Also, this course covers how to productionalize machine learning solutions by using Vertex AI.

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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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Під час курсу ви зможете ознайомитися з продуктами й сервісами Google Cloud для роботи з масивами даних і машинним навчанням, які підтримують життєвий цикл роботи з даними для тренування моделей штучного інтелекту. У курсі розглядаються процеси, проблеми й переваги створення конвеєру масиву даних і моделей машинного навчання з Vertex AI у Google Cloud.

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