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Selvakumar Arumugam

Учасник із 2020

Machine Learning Operations (MLOps) with Agent Platform: Model Evaluation Earned бер. 19, 2026 EDT
Build a Smart Cloud Application with Vibe Coding and MCP Earned бер. 19, 2026 EDT
Machine Learning Operations (MLOps) for Generative AI Earned бер. 18, 2026 EDT
Create Generative AI Apps on Google Cloud Earned бер. 12, 2026 EDT
Develop Gen AI Apps with Gemini and Streamlit Earned бер. 12, 2026 EDT
Natural Language Processing on Google Cloud Earned лют. 23, 2026 EST
Prompt Design in Agent Platform Earned лют. 17, 2026 EST
Responsible AI: Applying AI Principles with Google Cloud - Yкраїнська Earned лют. 16, 2026 EST
[DEPRECATED] Google Cloud: Prompt Engineering Guide Earned лют. 10, 2026 EST
Introduction to AI and Machine Learning on Google Cloud Earned лют. 7, 2026 EST
Introduction to Responsible AI - Українська Earned лют. 1, 2026 EST
Introduction to Large Language Models - Українська Earned лют. 1, 2026 EST
[DEPRECATED] Gemini for Cloud Architects Earned трав. 21, 2025 EDT
Google Cloud Fundamentals: Core Infrastructure - Yкраїнська Earned черв. 4, 2024 EDT
Scaling with Google Cloud Operations Earned трав. 8, 2024 EDT
Networking in Google Cloud: Fundamentals Earned трав. 3, 2024 EDT
Implement Sensitive Data Protection on Google Cloud Earned квіт. 26, 2024 EDT
Networking in Google Cloud: Routing and Addressing Earned квіт. 26, 2024 EDT
Build a Secure Google Cloud Network Earned квіт. 25, 2024 EDT
Implement Cloud Security Fundamentals on Google Cloud Earned квіт. 25, 2024 EDT
Mitigating Security Vulnerabilities on Google Cloud Earned квіт. 25, 2024 EDT
Security & Identity Fundamentals Earned лют. 12, 2024 EST
Manage Security in Google Cloud Earned лют. 12, 2024 EST
Build a Certification Study Guide: PCSE Exam Prep Earned лют. 5, 2024 EST
Introduction to Generative AI - Українська Earned січ. 16, 2024 EST
Attention Mechanism Earned вер. 27, 2023 EDT
Introduction to Image Generation Earned вер. 27, 2023 EDT
Build a Certification Study Guide: PDE Exam Prep Earned черв. 19, 2023 EDT
Engineer Data for Predictive Modeling with BigQuery ML Earned черв. 15, 2023 EDT
Підготовка даних для інтерфейсів API машинного навчання в Google Cloud Earned черв. 15, 2023 EDT
Create ML Models with BigQuery ML Earned черв. 11, 2023 EDT
Smart Analytics, Machine Learning, and AI on Google Cloud Earned черв. 7, 2023 EDT
Build Streaming Data Pipelines on Google Cloud Earned трав. 26, 2023 EDT
Build Batch Data Pipelines on Google Cloud Earned трав. 22, 2023 EDT
Build a Data Warehouse with BigQuery Earned трав. 8, 2023 EDT
Build Data Lakes and Data Warehouses on Google Cloud Earned трав. 6, 2023 EDT
Derive Insights from BigQuery Data Earned квіт. 30, 2023 EDT
Google Cloud Big Data and Machine Learning Fundamentals - українська Earned квіт. 29, 2023 EDT
Managing Cloud Infrastructure with Terraform Earned січ. 18, 2021 EST
Налаштування мережі Google Cloud Earned січ. 18, 2021 EST
Налаштування середовища для розробки додатка в Google Cloud Earned груд. 8, 2020 EST
DEPRECATED Application Development - Python Earned груд. 8, 2020 EST
Cloud Development Earned груд. 8, 2020 EST
Automate Deployment and Manage Traffic on a Google Cloud Network Earned груд. 7, 2020 EST
Deploy Kubernetes Applications on Google Cloud Earned лист. 29, 2020 EST
Implement DevOps Workflows in Google Cloud Earned лист. 29, 2020 EST
Налаштування Cloud Load Balancing для Compute Engine Earned лист. 23, 2020 EST
Початок роботи з інфраструктурою Earned лист. 23, 2020 EST
Kubernetes in Google Cloud Earned лист. 23, 2020 EST
DevOps Essentials Earned лист. 23, 2020 EST
DEPRECATED Cloud Architecture Earned лист. 23, 2020 EST
Розробка хмарних технологій Earned лист. 22, 2020 EST
Основи Google Cloud Earned лист. 20, 2020 EST
Build a Secure Google Cloud Network Earned лист. 19, 2020 EST
Cloud Architecture: Design, Implement, and Manage Earned лист. 19, 2020 EST

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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Earn a skill badge by completing the Build a Smart Cloud Application with Vibe Coding and MCP course, where you will learn to leverage the power of Google's AI coding assistant and MCP servers. Explore other content in the Gemini Enterprise Agent Ready (GEAR) program.

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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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Generative AI applications can create new user experiences that were nearly impossible before the invention of large language models (LLMs). As an application developer, how can you use generative AI to build engaging, powerful apps on Google Cloud? In this course, you'll learn about generative AI applications and how you can use prompt design and retrieval augmented generation (RAG) to build powerful applications using LLMs. You'll learn about a production-ready architecture that can be used for generative AI applications and you'll build an LLM and RAG-based chat application. Explore other content in the Gemini Agent Ready (GEAR) program.

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Complete the intermediate Develop Gen AI Apps with Gemini and Streamlit skill badge course to demonstrate skills in text generation, applying function calls with the Python SDK and Gemini API, and deploying a Streamlit application with Cloud Run. In this course, you learn Gemini prompting, test Streamlit apps in Cloud Shell, and deploy them as Docker containers in Cloud Run. Explore other content in the Gemini Enterprise Agent Ready (GEAR) program.

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This course introduces the products and solutions to solve NLP problems on Google Cloud. Additionally, it explores the processes, techniques, and tools to develop an NLP project with neural networks by using Vertex AI and TensorFlow.

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Complete the introductory Prompt Design in Agent Platform skill badge to demonstrate skills in the following: prompt engineering, image analysis, and multimodal generative techniques, within Agent Platform. Discover how to craft effective prompts, guide generative AI output, and apply Gemini models to real-world marketing scenarios. Explore other content in the Gemini Enterprise Agent Ready (GEAR) program.

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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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Google Cloud : Prompt Engineering Guide examines generative AI tools, how they work. We'll explore how to combine Google Cloud knowledge with prompt engineering to improve Gemini responses.

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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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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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In this course, you learn how Gemini, a generative AI-powered collaborator from Google Cloud, helps administrators provision infrastructure. You learn how to prompt Gemini to explain infrastructure, deploy GKE clusters and update existing infrastructure. Using a hands-on lab, you experience how Gemini improves the GKE deployment workflow. Duet AI was renamed to Gemini, our next-generation model.

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Курс "Знайомство з Google Cloud: основна інфраструктура" охоплює важливі поняття й терміни щодо використання Google Cloud. Переглядаючи відео й виконуючи практичні завдання, слухачі ознайомляться з різними сервісами Google Cloud для обчислень і зберігання даних, а також важливими ресурсами й інструментами для керування правилами. Крім того, вони зможуть їх порівнювати.

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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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Networking in Google cloud is a 6 part course series. Welcome to the first course of our six part course series, Networking in Google Cloud: Fundamentals.  This course provides a comprehensive overview of core networking concepts, including networking fundamentals, virtual private clouds (VPCs), and the sharing of VPC networks. Additionally, the course covers network logging and monitoring techniques. 

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Complete the Implement Sensitive Data Protection on Google Cloud Skill Badge course to demonstrate skills in the following: using Sensitive Data Protection services (including the Cloud Data Loss Prevention API) to inspect, redact, and de-identify sensitive data in Google Cloud.

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Welcome to the second course in the networking and Google Cloud series routing and addressing. In this course, we'll cover the central routing and addressing concepts that are relevant to Google Cloud's networking capabilities. Module one will lay the foundation by exploring network routing and addressing in Google Cloud, covering key building blocks such as routing IPv4, bringing your own IP addresses and setting up cloud DNS. In Module two will shift our focus to private connection options, exploring use cases and methods for accessing Google and other services privately using internal IP addresses. By the end of this course, you'll have a solid grasp of how to effectively route and address your network traffic within Google Cloud.

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Earn a skill badge by completing the Build a Secure Google Cloud Network skill badge course, where you will learn about multiple networking-related resources to build, scale, and secure your applications on Google Cloud.

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

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In this self-paced training course, participants learn mitigations for attacks at many points in a Google Cloud-based infrastructure, including Distributed Denial-of-Service attacks, phishing attacks, and threats involving content classification and use. They also learn about the Security Command Center, cloud logging and audit logging, and using Forseti to view overall compliance with your organization's security policies.

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Security is an uncompromising feature of Google Cloud services, and Google Cloud has developed specific tools for ensuring safety and identity across your projects. In this fundamental-level quest, you will get hands-on practice with Google Cloud’s Identity and Access Management (IAM) service, which is the go-to for managing user and virtual machine accounts. You will get experience with network security by provisioning VPCs and VPNs, and learn what tools are available for security threat and data loss protections.

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This course provides foundational knowledge for securing your Google Cloud environment. You will learn Google Cloud's security model, including Zero Trust principles and the Shared Fate model. You will configure identity services like Cloud Identity and Workforce Identity Federation, manage access control using Identity and Access Management (IAM) roles, conditions, and policies, and implement network security controls such as VPCs, Cloud Firewall tiers, and VPC Service Controls to build a secure perimeter. As the foundation of the Security in Google Cloud learning path, this course establishes the core principles required for two subsequent specialized tracks: the five-course Security Best Practices in Google Cloud series and the four-course Mitigate Threats in Google Cloud series.

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Learn how to use Gemini Notebook to create a personalized study guide for the Professional Cloud Security Engineer certification exam. You'll review Gemini Notebook features, create a notebook, and learn how to use a study guide to practice for the certification exam.

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

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This course 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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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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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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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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Complete the intermediate Create ML Models with BigQuery ML skill badge to demonstrate skills in creating and evaluating machine learning models with BigQuery ML to make data predictions.

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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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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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In this intermediate course, you will learn to design, build, and optimize robust batch data pipelines on Google Cloud. Moving beyond fundamental data handling, you will explore large-scale data transformations and efficient workflow orchestration, essential for timely business intelligence and critical reporting. Get hands-on practice using Dataflow for Apache Beam and Serverless for Apache Spark (Dataproc Serverless) for implementation, and tackle crucial considerations for data quality, monitoring, and alerting to ensure pipeline reliability and operational excellence. A basic knowledge of data warehousing, ETL/ELT, SQL, Python, and Google Cloud concepts is recommended.

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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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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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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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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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In this Quest, the experienced user of Google Cloud will learn how to describe and launch cloud resources with Terraform, an open source tool that codifies APIs into declarative configuration files that can be shared amongst team members, treated as code, edited, reviewed, and versioned. In these nine hands-on labs, you will work with example templates and understand how to launch a range of configurations, from simple servers, through full load-balanced applications.

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Щоб отримати кваліфікаційний значок, пройдіть курс Налаштування мережі Google Cloud. У ньому ви дізнаєтеся про різні способи розгортання й моніторингу додатків, зокрема навчитеся визначати ролі керування ідентифікацією і доступом, надавати або вилучати доступ до проектів, створювати мережі VPC, розгортати й відстежувати віртуальні машини Compute Engine, писати запити SQL, а також по-різному вводити додатки в дію за допомогою Kubernetes.

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Щоб отримати кваліфікаційний значок, пройдіть курс Налаштування середовища для розробки додатка в Google Cloud. У ньому ви навчитеся створювати й підключати хмарну інфраструктуру, спрямовану на зберігання даних, за допомогою базових можливостей таких технологій, як Cloud Storage, система керування ідентифікацією і доступом, Cloud Functions та Pub/Sub.

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In this advanced-level quest, you will learn the ins and outs of developing GCP applications in Python. The first labs will walk you through the basics of environment setup and application data storage with Cloud Datastore. Once you have a handle on the fundamentals, you will get hands-on practice deploying Python applications on Kubernetes and App Engine (the latter is the same framework that powers Snapchat!) With specialized bonus labs that teach user authentication and backend service development, this quest will give you practical experience so you can start developing robust Python applications straight away.

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The hands-on labs in this Quest are structured to give experienced app developers hands-on practice with the state-of-the-art developing applications in Google Cloud. The topics align with the Google Cloud Certified Professional Cloud Developer Certification. These labs follow the sequence of activities needed to create and deploy an app in Google Cloud from beginning to end. Be aware that while practice with these labs will increase your skills and abilities, it is recommended that you also review the exam guide and other available preparation resources.

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Networking is a principle theme of cloud computing. It’s the underlying structure of Google Cloud, and it’s what connects all your resources and services to one another. This course will cover essential Google Cloud networking services and will give you hands-on practice with specialized tools for developing mature networks. From learning the ins-and-outs of VPCs, to creating enterprise-grade load balancers, Automate Deployment and Manage Traffic on a Google Cloud Network will give you the practical experience needed so you can start building robust networks right away.

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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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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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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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Якщо ви лише пробуєте розробляти хмарні рішення й шукаєте практичні заняття на додаток до кваліфікаційного курсу "Знайомство з Google Cloud", тоді цей курс саме для вас. Ви отримаєте прикладний досвід завдяки практичним заняттям, присвяченим Cloud Storage і іншим ключовим сервісам додатків, як-от Monitoring і Cloud Functions. Ви отримаєте цінні навички, які можна застосовувати в будь-яких проєктах Google Cloud.

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Kubernetes is the most popular container orchestration system, and Google Kubernetes Engine was designed specifically to support managed Kubernetes deployments in Google Cloud. In this course, you will get hands-on practice configuring Docker images, containers, and deploying fully-fledged Kubernetes Engine applications.

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Obtain a competitive advantage through DevOps. DevOps is an organizational and cultural movement that aims to increase software delivery velocity, improve service reliability, and build shared ownership among software stakeholders. In this course you will learn how to use Google Cloud to improve the speed, stability, availability, and security of your software delivery capability. DevOps Research and Assessment has joined Google Cloud. How does your team measure up? Take this five question multiple-choice quiz and find out!

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This fundamental-level quest is unique amongst the other quest offerings. The labs have been curated to give IT professionals hands-on practice with topics and services that appear in the Google Cloud Certified Professional Cloud Architect Certification. From IAM, to networking, to Kubernetes engine deployment, this quest is composed of specific labs that will put your Google Cloud knowledge to the test. Be aware that while practice with these labs will increase your skills and abilities, we recommend that you also review the exam guide and other available preparation resources.

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Цей вступний курс унікальний серед інших курсів. Практичні заняття розроблено так, щоб ІТ-фахівці могли опрацювати теми й сервіси, які ввійдуть до сертифікації Google Cloud Certified Associate Cloud Engineer. У цей курс входять спеціально розроблені практичні заняття, зокрема присвячені керуванню ідентифікацією і доступом, організації мереж і розгортанню Kubernetes Engine, щоб ви могли перевірити свої знання про Google Cloud. Майте на увазі, що хоча виконання цих практичних занять покращить ваші навички й уміння, радимо також переглянути посібник із підготовки до іспиту й інші доступні ресурси.

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На цьому вступному курсі ви отримаєте практичний досвід роботи з основними інструментами й сервісами Google Cloud. Також вам доступні додаткові відео, які допоможуть краще зрозуміти контекст і закріпити знання щодо тем, розглянутих у практичних заняттях. Google Cloud "Основи" – це рекомендований початковий курс для тих, хто починає вивчати Google Cloud. Ви можете розпочати навчання, маючи мінімальні знання про хмарні технології або взагалі не маючи їх, і отримати практичний досвід, який зможете застосувати у своєму першому хмарному проєкті Google Cloud. "Основи Google Cloud" дають вичерпне уявленні про ключові функції платформи – від написання команд у Cloud Shell і розгортання першої віртуальної машини до запуску додатків на Kubernetes Engine і використання розподілу навантаження.

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Earn a skill badge by completing the Build a Secure Google Cloud Network skill badge course, where you will learn about multiple networking-related resources to build, scale, and secure your applications on Google Cloud.

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Earn a skill badge by completing the Cloud Architecture: Design, Implement, and Manage to demonstrate skills in the following: deploy a publicly accessible website using Apache web servers, configure a Compute Engine VM using startup scripts, configure secure RDP using a Windows Bastion host and firewall rules, build and deploy a Docker image to a Kubernetes cluster and then update it, and create a CloudSQL instance and import a MySQL database. This skill badge is a great resource for understanding topics that will appear in the Google Cloud Certified Professional Cloud Architect certification exam.

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