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Mykhailo Prochkaruk
Member since 2022
Diamond League
29698 points
Develop Gen AI Apps with Gemini and Streamlit
Earned Eki 18, 2025 EDT
Çoklu Format Destekli Gemini ve Çok Formatlı RAG ile Zengin Belgeleri İnceleme
Earned Eki 18, 2025 EDT
Explore Generative AI in Agent Platform
Earned Eki 17, 2025 EDT
Build Custom Processors with Document AI [Deprecated]
Earned Eki 17, 2025 EDT
Build Real World AI Applications with Gemini and Imagen
Earned Eki 17, 2025 EDT
Agent Platform'da İstem Tasarımı
Earned Eki 17, 2025 EDT
Build Event-Driven Applications with Eventarc
Earned Eki 16, 2025 EDT
Store, Process, and Manage Data on Google Cloud - Console
Earned Eki 16, 2025 EDT
Monitor and Manage Google Cloud Resources
Earned Eki 16, 2025 EDT
Deploy and Secure Serverless APIs with API Gateway
Earned Eki 16, 2025 EDT
Implement Cloud Collaboration and Productivity Workflows
Earned Eki 15, 2025 EDT
Deploy and Manage Applications on Google App Engine
Earned Eki 15, 2025 EDT
Build Serverless Applications with Cloud Run Functions
Earned Eki 15, 2025 EDT
Implement Cloud Storage and Data Protection Solutions
Earned Eki 15, 2025 EDT
Implement Event-Driven Messaging and Automation Workflows
Earned Eki 15, 2025 EDT
Görüntülere Altyazı Ekleme Modelleri Oluşturma
Earned Tem 10, 2023 EDT
Vertex AI Studio'ya Giriş
Earned Tem 10, 2023 EDT
Dönüştürücü Modelleri ve BERT Modeli
Earned Tem 10, 2023 EDT
Dikkat Mekanizması
Earned Tem 10, 2023 EDT
Kodlayıcı-Kod Çözücü Mimarisi
Earned Tem 10, 2023 EDT
Görüntü Üretmeye Giriş
Earned Tem 10, 2023 EDT
Generative AI Fundamentals
Earned Tem 10, 2023 EDT
[DEPRECATED] Sorumlu Yapay Zekaya Giriş
Earned Tem 10, 2023 EDT
Büyük Dil Modellerine Giriş
Earned Tem 10, 2023 EDT
Üretken Yapay Zekaya Giriş
Earned May 22, 2023 EDT
Create and Manage AlloyDB Instances
Earned May 22, 2023 EDT
DEPRECATED Build Google Cloud Infrastructure for Azure Professionals
Earned May 17, 2023 EDT
Automate Data Capture at Scale with Document AI
Earned May 17, 2023 EDT
Monitor Environments with Google Cloud Managed Service for Prometheus
Earned May 16, 2023 EDT
Create and Manage Cloud Spanner Instances
Earned May 14, 2023 EDT
Create and Manage Bigtable Instances
Earned May 14, 2023 EDT
Deploy and Manage Apigee X
Earned May 14, 2023 EDT
Develop and Secure APIs with Apigee X
Earned May 14, 2023 EDT
Build Google Cloud Infrastructure for AWS Professionals
Earned May 13, 2023 EDT
Implement CI/CD Pipelines on Google Cloud
Earned May 13, 2023 EDT
Monitor and Log with Google Cloud Observability
Earned May 13, 2023 EDT
Implement DevOps Workflows in Google Cloud
Earned May 12, 2023 EDT
Hybrid Cloud Modernizing Applications with Anthos
Earned Mar 23, 2023 EDT
ML Pipelines on Google Cloud
Earned Mar 23, 2023 EDT
Getting Started with Google Kubernetes Engine
Earned Mar 17, 2023 EDT
Application Development with Cloud Run
Earned Mar 14, 2023 EDT
App Deployment, Debugging, and Performance
Earned Mar 10, 2023 EST
Securing and Integrating Components of your Application
Earned Mar 9, 2023 EST
Getting Started With Application Development
Earned Mar 4, 2023 EST
DEPRECATED Build and Deploy Machine Learning Solutions on Vertex AI
Earned Mar 2, 2023 EST
Use Machine Learning APIs on Google Cloud
Earned Şub 28, 2023 EST
Google Cloud Computing Foundations: Data, ML, and AI in Google Cloud
Earned Şub 26, 2023 EST
[DEPRECATED] - Google Cloud Computing Foundations: Networking and Security in Google Cloud - Locales
Earned Şub 26, 2023 EST
[DEPRECATED] Google Cloud Computing Foundations: Infrastructure in Google Cloud - Locales
Earned Şub 26, 2023 EST
[DEPRECATED]-Google Cloud Computing Foundations: Cloud Computing Fundamentals - Turkish
Earned Şub 26, 2023 EST
Migrate MySQL Data to Cloud SQL Using Database Migration Service
Earned Şub 26, 2023 EST
Recommendation Systems on Google Cloud
Earned Şub 25, 2023 EST
Google Cloud Computing Foundations: Data, ML, and AI in Google Cloud - Locales
Earned Şub 24, 2023 EST
DEPRECATED Detect Manufacturing Defects Using Visual Inspection AI
Earned Şub 24, 2023 EST
Production Machine Learning Systems
Earned Şub 20, 2023 EST
Machine Learning Operations (MLOps): Getting Started
Earned Şub 19, 2023 EST
Automate Interactions with Contact Center AI
Earned Şub 19, 2023 EST
Google Cloud'da Makine Öğrenimi API'leri İçin Veri Hazırlama
Earned Şub 18, 2023 EST
Natural Language Processing on Google Cloud
Earned Şub 18, 2023 EST
Computer Vision Fundamentals with Google Cloud
Earned Şub 17, 2023 EST
Machine Learning in the Enterprise
Earned Şub 15, 2023 EST
Engineer Data for Predictive Modeling with BigQuery ML
Earned Şub 14, 2023 EST
Create ML Models with BigQuery ML
Earned Şub 13, 2023 EST
Build a Data Warehouse with BigQuery
Earned Şub 12, 2023 EST
[DEPRECATED] Feature Engineering
Earned Şub 12, 2023 EST
Prepare Data for Looker Dashboards and Reports
Earned Şub 12, 2023 EST
Perform Predictive Data Analysis in BigQuery
Earned Şub 12, 2023 EST
Google Cloud Ağınızı Geliştirme
Earned Şub 11, 2023 EST
Set Up a Google Cloud Network
Earned Şub 11, 2023 EST
Share Data Using Google Data Cloud
Earned Şub 11, 2023 EST
Implement Cloud Security Fundamentals on Google Cloud
Earned Şub 11, 2023 EST
BigQuery Verilerinden Analiz Elde Etme
Earned Şub 11, 2023 EST
[Depricated] Build, Train and Deploy ML Models with Keras on Google Cloud
Earned Şub 10, 2023 EST
Launching into Machine Learning
Earned Şub 9, 2023 EST
How Google Does Machine Learning
Earned Şub 6, 2023 EST
[DEPRECATED] Google Cloud Big Data and Machine Learning Fundamentals
Earned Şub 5, 2023 EST
Develop Serverless Applications on Cloud Run
Earned Eki 16, 2022 EDT
Create and Manage Cloud SQL for PostgreSQL Instances
Earned Eki 15, 2022 EDT
Cloud SQL
Earned Eki 15, 2022 EDT
Develop Serverless Apps with Firebase
Earned Eki 14, 2022 EDT
Güvenli Bir Google Cloud Ağı Oluşturma
Earned Eki 14, 2022 EDT
API Design and Fundamentals with the Apigee API Platform
Earned Eki 14, 2022 EDT
Build Infrastructure with Terraform on Google Cloud
Earned Eki 13, 2022 EDT
Networking in Google Cloud: Fundamentals
Earned Eki 13, 2022 EDT
Getting Started with Terraform for Google Cloud
Earned Eki 12, 2022 EDT
Optimize Costs for Google Kubernetes Engine
Earned Eki 9, 2022 EDT
Google Cloud'da Web Sitesi Derleme
Earned Eki 9, 2022 EDT
Google Cloud'da Uygulama Geliştirme Ortamı Oluşturma
Earned Eki 8, 2022 EDT
Cloud Architecture: Design, Implement, and Manage
Earned Eki 8, 2022 EDT
Deploy Kubernetes Applications on Google Cloud
Earned Eki 8, 2022 EDT
Compute Engine İçin Cloud Load Balancing'i Uygulama
Earned Eki 8, 2022 EDT
Google Cloud Fundamentals: Core Infrastructure
Earned Eki 8, 2022 EDT
Google Cloud'un Temel Özellikleri
Earned Eki 5, 2022 EDT
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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Orta düzeydeki Çoklu Format Destekli Gemini ve Çok Formatlı RAG ile Zengin Belgeleri İnceleme beceri rozetini tamamlayarak şu konulardaki becerilerinizi kanıtlayabilirsiniz: Çok formatlı istemler kullanarak metin ve görsel formatlarındaki verilerden bilgi elde etme, video açıklaması oluşturabilme ve Gemini ile çok formatlılıktan yararlanarak videonun kapsamındaki bilgilerden çok daha fazlasına ulaşabilme; metin ve görüntü içeren dokümanların meta verilerini oluşturma, gerekli tüm metin parçalarına ulaşma ve Gemini'ın Çok Formatlı Almayla Artırılmış Üretim (RAG) mimarisini kullanarak alıntıları yazdırma.
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Complete the intermediate Explore Generative AI in Agent Platform skill badge to demonstrate skills in text generation, image and video analysis for enhanced content creation, and applying function calling techniques within the Gemini API. Discover how to leverage sophisticated Gemini techniques, explore multimodal content generation, and expand the capabilities of your AI-powered projects. Explore other content in the Gemini Enterprise Agent Ready (GEAR) program.
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Earn a skill badge by completing the Build Custom Processors with Document AI course. You learn how to extract data and classify documents by creating custom ML models specific to your business needs. This course teaches the foundation skills of building your own processors, working with optical character recognition, form parsing, processor creation, and uptraining the DocumentAI model.
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Complete the introductory Build Real World AI Applications with Gemini and Imagen skill badge to demonstrate skills in the following: image recognition, natural language processing, image generation using Google's powerful Gemini and Imagen models, deploying applications on the Google Cloud Agent Platform. Explore other content in the Gemini Enterprise Agent Ready (GEAR) program.
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Agent Platform'da istem mühendisliği, görüntü analizi ve çok modlu üretken teknikler gibi becerileri göstermek için Agent Platform'da İstem Tasarımı beceri rozetini tamamlayın. Etkili istemlerin nasıl oluşturulacağını, üretken yapay zeka çıktılarına nasıl rehberlik edileceğini ve Gemini modellerinin gerçek dünyadaki pazarlama senaryolarına nasıl uygulanacağını keşfedin.
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Earn a Skill Badge by completing the Build Event-Driven Applications with Eventarc course. In this course, you use Eventarc to create event triggers for different resources, including Pub/Sub topics and Cloud Storage buckets.
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Cloud Storage, Cloud Functions, and Cloud Pub/Sub are all Google Cloud Platform services that can be used to store, process, and manage data. All three services can be used together to create a variety of data-driven applications. In this skill badge you use Cloud Storage to store images, Cloud Functions to process the images, and Cloud Pub/Sub to send the images to another application.
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Complete the introductory Monitor and Manage Google Cloud Resources skill badge to demonstrate skills in the following: granting and revoking IAM permissions; installing monitoring and logging agents; creating, deploying, and testing an event-driven Cloud Run function.
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Earn a skill badge by completing the Deploy and Secure Serverless APIs with API Gateway skill badge course, where you learn how to use API Gateway to deploy, secure, and manage APIs with a fully managed gateway.
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Earn an introductory skill badge by completing the Implement Cloud Collaboration and Productivity Workflows course, where you will get introduced to Google's collaborative platform and learn to use Gmail, Calendar, Meet, Drive, Sheets, and AppSheet.
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Earn a Skill Badge by completing the Deploy and Manage Applications on Google App Engine course, where you learn how to use App Engine with Python, Go, and PHP.
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Earn a Introductory skill badge by completing the Build Serverless Applications with Cloud Run Functions course, where you learn how to use Cloud Run functions through the Google Cloud console and on the command line.
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Earn a Skill Badge by completing the Implement Cloud Storage and Data Protection Solutions course. Learn to create a Cloud Storage bucket, use the command line, and protect objects with Bucket Lock.
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Earn a skill badge by completing the Implement Event-Driven Messaging and Automation Workflows skill badge course, where you learn how to use Pub/Sub through the Cloud console, how Cloud Scheduler jobs can save you effort, and when Pub/Sub Lite can save you money on high-volume event ingestion.
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Bu kurs, derin öğrenmeyi kullanarak görüntülere altyazı ekleme modeli oluşturmayı öğretmektedir. Kurs sırasında görüntülere altyazı ekleme modelinin farklı bileşenlerini (ör. kodlayıcı ve kod çözücü) ve modelinizi eğitip değerlendirmeyi öğreneceksiniz. Bu kursu tamamlayan öğrenciler, kendi görüntülere altyazı ekleme modellerini oluşturabilecek ve bu modelleri görüntülere altyazı oluşturmak için kullanabilecek.
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Bu kursta Vertex AI Studio tanıtılmaktadır. Bu araç, üretken yapay zeka modelleriyle etkileşime geçmek, kurumsal fikirlerin prototipini oluşturmak ve bunları gerçek hayatta uygulamak için kullanılır. Gerçek hayattan kullanım alanları, etkileşimli dersler ve uygulamalı laboratuvarlar aracılığıyla, ilk istemden son ürüne uzanan yaşam döngüsünü keşfedecek ve çoklu format destekli Gemini uygulamaları, istem tasarımı, istem mühendisliği ve model ayarlama konularında Vertex AI Studio'dan nasıl yararlanabileceğinizi öğreneceksiniz. Bu kursun amacı, Vertex AI Studio'yu kullanarak projelerinizde üretken yapay zekadan yararlanabilmenizi sağlamaktır.
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Bu kurs, dönüştürücü mimarisini ve dönüştürücülerden çift yönlü kodlayıcı temsilleri (BERT - Encoder Representations from Transformers) modelini tanıtmaktadır. Kursta, öz dikkat mekanizması gibi dönüştürücü mimarisinin ana bileşenlerini ve BERT modelini oluşturmak için dönüştürücünün nasıl kullanıldığını öğreneceksiniz. Ayrıca sınıflandırma, soru yanıtlama ve doğal dil çıkarımı gibi BERT'in kullanılabileceği çeşitli görevler hakkında da bilgi sahibi olacaksınız. Kursun tahmini süresi 45 dakikadır.
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Bu kursta nöral ağların, giriş sırasının belirli bölümlerine odaklanmasına olanak tanıyan güçlü bir teknik olan dikkat mekanizması tanıtılmaktadır. Kursta, dikkat mekanizmasının çalışma şeklini ve makine öğrenimi, metin özetleme ve soru yanıtlama gibi çeşitli makine öğrenimi görevlerinin performansını artırmak için nasıl kullanılabileceğini öğreneceksiniz.
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Bu kursta, kodlayıcı-kod çözücü mimarisi özet olarak anlatılmaktadır. Bu mimari; makine çevirisi, metin özetleme ve soru yanıtlama gibi "sıradan sıraya" görevlerde yaygın olarak kullanılan, güçlü bir makine öğrenimi mimarisidir. Kursta, kodlayıcı-kod çözücü mimarisinin ana bileşenlerini ve bu modellerin nasıl eğitilip sunulacağını öğreneceksiniz. Laboratuvarın adım adım açıklamalı kılavuz bölümünde ise sıfırdan şiir üretmek için TensorFlow'da kodlayıcı-kod çözücü mimarisinin basit bir uygulamasını yazacaksınız.
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Bu kursta, görüntü üretme alanında gelecek vadeden bir makine öğrenimi modelleri ailesi olan "difüzyon modelleri" tanıtılmaktadır. Difüzyon modelleri fizikten, özellikle de termodinamikten ilham alır. Geçtiğimiz birkaç yıl içinde, gerek araştırma gerekse endüstri alanında difüzyon modelleri popülerlik kazandı. Google Cloud'daki son teknoloji görüntü üretme model ve araçlarının çoğu, difüzyon modelleri ile desteklenmektedir. Bu kursta, difüzyon modellerinin ardındaki teori tanıtılmakta ve bu modellerin Vertex AI'da nasıl eğitilip dağıtılacağı açıklanmaktadır.
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Earn a skill badge by completing the Introduction to Generative AI, Introduction to Large Language Models and Introduction to Responsible AI courses. 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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Giriş seviyesindeki bu mikro öğrenme kursu, sorumlu yapay zekanın ne olduğunu, neden önemli olduğunu ve Google'ın sorumlu yapay zekayı ürünlerinde nasıl uyguladığını açıklamayı amaçlamaktadır. Ayrıca Google'ın 3 yapay zeka ilkesini de tanıtır.
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Giriş seviyesindeki bu mikro öğrenme kursunda; büyük dil modellerinin (LLM) ne olduğu, hangi kullanım alanlarında kullanılabilecekleri ve istem ayarlama ile LLM performansını nasıl artırabileceğiniz ele alınmaktadır. Ayrıca kendi üretken yapay zeka uygulamalarınızı geliştirebileceğiniz Google araçları da yer almaktadır.
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Bu giriş seviyesi mikro öğrenme kursu, üretken yapay zekanın ne olduğunu, nasıl kullanıldığını ve geleneksel makine öğrenimi yöntemlerinden farkını açıklamayı amaçlamaktadır. Ayrıca kendi üretken yapay zeka uygulamalarınızı geliştirebileceğiniz Google araçları da ele alınmaktadır.
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Complete the introductory Create and Manage AlloyDB Instances skill badge to demonstrate skills in the following: performing core AlloyDB operations and tasks, migrating to AlloyDB from PostgreSQL, administering an AlloyDB database, and accelerating analytical queries using the AlloyDB Columnar Engine.
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Earn a skill badge by completing the Build Google Cloud Infrastructure for Azure Professionals course, where you learn how to configure IAM permission, orchestrate workloads using Kubernetes, host a web application using compute engine, and configure load balancing. A skill badge is an exclusive digital badge issued by Google Cloud in recognition of your proficiency with Google Cloud products and services and tests your ability to apply your knowledge in an interactive hands-on environment. Complete this Skill Badge, and the final assessment challenge lab, to receive a digital badge that you can share with your network.
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Earn the introductory skill badge by completing the Automate Data Capture at Scale with Document AI course. In this course, you learn how to extract, process, and capture data using Document AI.
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Earn a skill badge by completing the Monitor Environments with Google Cloud Managed Service for Prometheus skill badge course, where you learn Kubernetes Monitoring with Google Cloud Managed Service for Prometheus.
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Complete the introductory Create and Manage Cloud Spanner Instances skill badge to demonstrate skills in the following: creating and interacting with Cloud Spanner instances and databases; loading Cloud Spanner databases using various techniques; backing up Cloud Spanner databases; defining schemas and understanding query plans; and deploying a Modern Web App connected to a Cloud Spanner instance.
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Complete the introductory Create and Manage Bigtable Instances skill badge to demonstrate skills in the following: creating instances, designing schemas, querying data, and performing administrative tasks in Bigtable including monitoring performance and configuring node autoscaling and replication.
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Earn a skill badge by completing the Deploy and Manage Apigee X skill badge course, where you learn about the Apigee X architecture, how to provision an Apigee X organization within a Google Cloud project, the management of Apigee X using the Apigee API and UI, and the use of Cloud Armor and Apigee threat protection policies to protect your APIs.
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Earn a skill badge by completing the Develop and Secure APIs with Apigee X skill badge course, where you learn how to modernize your APIs, use service accounts and Google Authentication to securely access backend services from Apigee API proxies, productize APIs using API products and developer portals, secure APIs using features like API keys, OAuth, private variables and fault handling, integrate Apigee with Google Cloud services like Pub/Sub and Cloud Logging, and call Google Cloud APIs like the Natural Language API and the Geocoding API.
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Earn a skill badge by completing the Build Google Cloud Infrastructure for AWS Professionals course, where you learn how to configure IAM permission, orchestrate workloads using Kubernetes, host a web application using compute engine, and configure load balancing.
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Earn the intermediate skill badge by completing the Implement CI/CD Pipelines on Google Cloud skill badge course where you learn how to use Artifact Registry, Cloud Build, and Cloud Deploy. You interact with the Google Cloud console, Google Cloud CLI, Cloud Run, and GKE. This course teaches you how to build continuous integration pipelines, store and secure artifacts, scan for vulnerabilities, attest to the validity of approved releases. Additionally, you get hands-on experience deploying applications to both GKE and Cloud Run.
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Complete the introductory Monitor and Log with Google Cloud Observability skill badge course to demonstrate skills in the following: monitoring virtual machines in Compute Engine, utilizing Cloud Monitoring for multi-project oversight, extending monitoring and logging capabilities to Cloud Functions, creating and sending custom application metrics, and configuring Cloud Monitoring alerts based on custom metrics.
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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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Course four of the Anthos series prepares students to consider multiple approaches for modernizing applications and services within Anthos environments. Topics include optimizing workloads on serverless platforms and migrating workloads to Anthos. This course is a continuation of course three, Anthos on Bare Metal, and assumes direct experience with the topics covered in that course.
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In this course, you will be learning from ML Engineers and Trainers who work with the state-of-the-art development of ML pipelines here at Google Cloud. The first few modules will cover about TensorFlow Extended (or TFX), which is Google’s production machine learning platform based on TensorFlow for management of ML pipelines and metadata. You will learn about pipeline components and pipeline orchestration with TFX. You will also learn how you can automate your pipeline through continuous integration and continuous deployment, and how to manage ML metadata. Then we will change focus to discuss how we can automate and reuse ML pipelines across multiple ML frameworks such as tensorflow, pytorch, scikit learn, and xgboost. You will also learn how to use another tool on Google Cloud, Cloud Composer, to orchestrate your continuous training pipelines. And finally, we will go over how to use MLflow for managing the complete machine learning life cycle.
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Welcome to the Getting Started with Google Kubernetes Engine course. If you're interested in Kubernetes, a software layer that sits between your applications and your hardware infrastructure, then you’re in the right place! Google Kubernetes Engine brings you Kubernetes as a managed service on Google Cloud.
The goal of this course is to introduce the basics of Google Kubernetes Engine, or GKE, as it’s commonly referred to, and how to get applications containerized and running in Google Cloud. The course starts with a basic introduction to Google Cloud, and is then followed by an overview of containers and Kubernetes, Kubernetes architecture, and Kubernetes operations.
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This course introduces you to fundamentals, practices, capabilities and tools applicable to modern cloud-native application development using Google Cloud Run. Through a combination of lectures, hands-on labs, and supplemental materials, you will learn how to on Google Cloud using Cloud Run.design, implement, deploy, secure, manage, and scale applications
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In this course, application developers learn how to design and develop cloud-native applications that seamlessly integrate components from the Google Cloud ecosystem. Through a combination of presentations, demos, and hands-on labs, participants learn how to create repeatable deployments by treating infrastructure as code, choose the appropriate application execution environment for an application, and monitor application performance.
Completing one version of each lab is required. Each lab is available in Node.js. In most cases, the same labs are also provided in Python or Java. You may complete each lab in whichever language you prefer.
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In this course, application developers learn how to design and develop cloud-native applications that seamlessly integrate managed services from Google Cloud. Through a combination of presentations, demos, and hands-on labs, participants learn how to develop more secure applications, implement federated identity management, and integrate application components by using messaging, event-driven processing, and API gateways. Completing one version of each lab is required. Each lab is available in Node.js. In most cases, the same labs are also provided in Python or Java. You may complete each lab in whichever language you prefer. This is the second course of the Developing Applications with Google Cloud series. After completing this course, enroll in the App Deployment, Debugging, and Performance course.
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In this course, application developers learn how to design and develop cloud-native applications that seamlessly integrate managed services from Google Cloud. Through a combination of presentations, demos, and hands-on labs, participants learn how to apply best practices for application development and use the appropriate Google Cloud storage services for object storage, relational data, caching, and analytics.
Completing one version of each lab is required. Each lab is available in Node.js. In most cases, the same labs are also provided in Python or Java. You may complete each lab in whichever language you prefer.
This is the first course of the Developing Applications with Google Cloud series. After completing this course, enroll in the Securing and Integrating Components of your Application course.
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Earn the intermediate skill badge by completing the Build and Deploy Machine Learning Solutions on Vertex AI skill badge course, where you learn how to use Google Cloud's Vertex AI platform, AutoML, and custom training services to train, evaluate, tune, explain, and deploy machine learning models.
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Earn the advanced skill badge by completing the Use Machine Learning APIs on Google Cloud course, where you learn the basic features for the following machine learning and AI technologies: Cloud Vision API, Cloud Translation API, and Cloud Natural Language API.
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The Google Cloud Computing Foundations courses are for individuals with little to no background or experience in cloud computing. They provide an overview of concepts central to cloud basics, big data, and machine learning, and where and how Google Cloud fits in.
By the end of the series of courses, learners will be able to articulate these concepts and demonstrate some hands-on skills.
The courses should be completed in the following order:
1. Google Cloud Computing Foundations: Cloud Computing Fundamentals
2. Google Cloud Computing Foundations: Infrastructure in Google Cloud
3. Google Cloud Computing Foundations: Networking and Security in Google Cloud
4. Google Cloud Computing Foundations: Data, ML, and AI in Google Cloud
This final course in the series reviews managed big data services, machine learning and its value, and how to demonstrate your skill set in Google Cloud further by earning Skill Badges.
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Google Cloud Computing Foundations kursunda, bulut bilişimi alanında daha önce çalışmamış veya bu konuda hiç deneyimi olmayan bireylere;
temel bulut kavramları, büyük veri ve makine öğrenimi gibi kavramlar ve Google Cloud’un bu kavramlarla hangi noktada, nasıl birlikte çalıştığı
ayrıntılı bir genel bakışla anlatılır.
Kursun sonunda öğrenciler bulut bilişimi, büyük veri ve makine öğrenimi konularında
fikir yürütüp bazı becerileri pratik olarak sergileyebilecek seviyeye ulaşacaktır.
Bu kurs, Google Cloud Computing Foundations adlı kurs serisinin bir parçasıdır.
Kurslar aşağıdaki sırayla tamamlanmalıdır:
Google Cloud Computing Foundations: Cloud Computing Fundamentals - Locales
Google Cloud Computing Foundations: Infrastructure in Google Cloud - Locales
Google Cloud Computing Foundations: Networking and Security in Google Cloud - Locales
Google Cloud Computing Foundations: Data, ML, and AI in Google Cloud - Locales
Bu üçüncü kursta güvenli ağlar oluşturma,…
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Google Cloud Computing Foundations kursunda, bulut bilişimi alanında daha önce çalışmamış veya bu konuda hiç deneyimi olmayan bireylere;
temel bulut kavramları, büyük veri ve makine öğrenimi gibi kavramlar ve Google Cloud’un bu kavramlarla hangi noktada, nasıl birlikte çalıştığı
ayrıntılı bir genel bakışla anlatılır.
Kursun sonunda öğrenciler bulut bilişimi, büyük veri ve makine öğrenimi konularında
fikir yürütüp bazı becerileri pratik olarak sergileyebilecek seviyeye ulaşacaktır.
Bu kurs, Google Cloud Computing Foundations adlı kurs serisinin bir parçasıdır.
Kurslar aşağıdaki sırayla tamamlanmalıdır:
Google Cloud Computing Foundations: Cloud Computing Fundamentals - Locales
Google Cloud Computing Foundations: Infrastructure in Google Cloud - Locales
Google Cloud Computing Foundations: Networking and Security in Google Cloud - Locales
Google Cloud Computing Foundations: Data, ML, and AI in Google Cloud - Locales
Bu ikinci kursta Google Cloud'da depol…
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Google Cloud Computing Foundations kursunda, bulut bilişimi alanında daha önce çalışmamış veya bu konuda hiç deneyimi olmayan bireylere;
temel bulut kavramları, büyük veri, makine öğrenimi gibi kavramlar ve Google Cloud'un bu kavramlarla hangi noktada,
nasıl birlikte çalıştığı ayrıntılı bir genel bakışla anlatılır.
Kursun sonunda öğrenciler bulut bilişimi, büyük veri ve makine öğrenimi konularında
fikir yürütüp bazı becerileri pratik olarak sergileyebilecek seviyeye ulaşacaktır.
Bu kurs, Google Cloud Computing Foundations adlı kurs serisinin bir parçasıdır.
Kurslar aşağıdaki sırayla tamamlanmalıdır:
Google Cloud Computing Foundations: Cloud Computing Fundamentals - Locales
Google Cloud Computing Foundations: Infrastructure in Google Cloud - Locales
Google Cloud Computing Foundations: Networking and Security in Google Cloud - Locales
Google Cloud Computing Foundations: Data, ML, and AI in Google Cloud - Locales
İlk kursta bulut bilişimi, Google Cloud'u k…
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Complete the introductory Migrate MySQL Data to Cloud SQL Using Database Migration Service skill badge course to demonstrate skills in the following: migrating MySQL data to Cloud SQL using different job types and connectivity options available in Database Migration Service and migrating MySQL user data when running Database Migration Service jobs.
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In this course, you apply your knowledge of classification models and embeddings to build a ML pipeline that functions as a recommendation engine.
This is the fifth and final course of the Advanced Machine Learning on Google Cloud series.
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Google Cloud Computing Foundations kursunda, bulut bilişimi alanında daha önce çalışmamış veya bu konuda hiç deneyimi olmayan bireylere;
temel bulut kavramları, büyük veri ve makine öğrenimi gibi kavramlar ve Google Cloud’un bu kavramlarla hangi noktada, nasıl birlikte çalıştığı
ayrıntılı bir genel bakışla anlatılır.
Kursun sonunda öğrenciler bulut bilişimi, büyük veri ve makine öğrenimi konularında
fikir yürütüp bazı becerileri pratik olarak sergileyebilecek seviyeye ulaşacaktır.
Bu kurs, Google Cloud Computing Foundations adlı kurs serisinin bir parçasıdır.
Kurslar aşağıdaki sırayla tamamlanmalıdır:
Google Cloud Computing Foundations: Cloud Computing Fundamentals - Locales
Google Cloud Computing Foundations: Infrastructure in Google Cloud - Locales
Google Cloud Computing Foundations: Networking and Security in Google Cloud - Locales
Google Cloud Computing Foundations: Data, ML, and AI in Google Cloud - Locales
Seride yer alan bu …
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Earn a skill badge by completing the Detect Manufacturing Defects using Visual Inspection AI course, where you learn how to use Visual Inspection AI to deploy a solution artifact and test that it can successfully identify defects in a manufacturing process.
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This course covers how to implement the various flavors of production ML systems— static, dynamic, and continuous training; static and dynamic inference; and batch and online processing. You delve into TensorFlow abstraction levels, the various options for doing distributed training, and how to write distributed training models with custom estimators.
This is the second course of the Advanced Machine Learning on Google Cloud series. After completing this course, enroll in the Image Understanding with TensorFlow on Google Cloud course.
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This course introduces participants to MLOps tools and best practices for deploying, evaluating, monitoring and operating production ML systems on Google Cloud. MLOps is a discipline focused on the deployment, testing, monitoring, and automation of ML systems in production. Machine Learning Engineering professionals use tools for continuous improvement and evaluation of deployed models. They work with (or can be) Data Scientists, who develop models, to enable velocity and rigor in deploying the best performing models.
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Earn a skill badge by completing the Automate Interactions with Contact Center AI quest, where you will learn about the features of Contact Center AI, including how to Build a virtual agent, Design conversation flows for your virtual agent; Add a phone gateway to your virtual agent; Use Dialogflow for troubleshooting; Review logs and debug your virtual agent. A skill badge is an exclusive digital badge issued by Google Cloud in recognition of your proficiency with Google Cloud products and services and tests your ability to apply your knowledge in an interactive hands-on environment. Complete the skill badge quest, and final assessment challenge lab, to receive a digital badge that you can share with your network.
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Giriş düzeyindeki Google Cloud'da Makine Öğrenimi API'leri İçin Veri Hazırlama beceri rozetini tamamlayarak şu konulardaki becerilerinizi gösterin: Dataprep by Trifacta ile veri temizleme, Dataflow'da veri ardışık düzenleri çalıştırma, Managed Service for Apache Spark'ta küme oluşturma ve Apache Spark işleri çalıştırma ve makine öğrenimi API'lerini (Cloud Natural Language API, Google Cloud Speech-to-Text API ve Video Intelligence API dahil olmak üzere) çağırma.
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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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This course describes different types of computer vision use cases and then highlights different machine learning strategies for solving these use cases. The strategies vary from experimenting with pre-built ML models through pre-built ML APIs and AutoML Vision to building custom image classifiers using linear models, deep neural network (DNN) models or convolutional neural network (CNN) models.
The course shows how to improve a model's accuracy with augmentation, feature extraction, and fine-tuning hyperparameters while trying to avoid overfitting the data.
The course also looks at practical issues that arise, for example, when one doesn't have enough data and how to incorporate the latest research findings into different models.
Learners will get hands-on practice building and optimizing their own image classification models on a variety of public datasets in the labs they will work on.
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This course takes a real-world approach to the ML Workflow through a case study. An ML team faces several ML business requirements and use cases. The team must understand the tools required for data management and governance and consider the best approach for data preprocessing.
The team is presented with three options to build ML models for two use cases. The course explains why they would use AutoML, BigQuery ML, or custom training to achieve their objectives.
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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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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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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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This course explores the benefits of using Vertex AI Feature Store, how to improve the accuracy of ML models, and how to find which data columns make the most useful features. This course also includes content and labs on feature engineering using BigQuery ML, Keras, and TensorFlow.
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Complete the introductory Prepare Data for Looker Dashboards and Reports skill badge course to demonstrate skills in the following: filtering, sorting, and pivoting data; merging results from different Looker Explores; and using functions and operators to build Looker dashboards and reports for data analysis and visualization.
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Complete the intermediate Perform Predictive Data Analysis in BigQuery skill badge course to demonstrate skills in the following: creating datasets in BigQuery by importing CSV and JSON files; harnessing the power of BigQuery with sophisticated SQL analytical concepts, including using BigQuery ML to train an expected goals model on soccer event data and evaluate the impressiveness of World Cup goals.
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Google Cloud Ağınızı Geliştirme kursunu tamamlayarak bir beceri rozeti kazanın. IAM rollerini keşfetme ve proje erişimi ekleme/kaldırma, VPC ağları oluşturma, Compute Engine sanal makinelerini dağıtma ve izleme, SQL sorguları yazma ve çeşitli dağıtım yaklaşımlarıyla Kubernetes'i kullanarak uygulama dağıtma gibi uygulamaları dağıtıp izlemeyle ilgili birden çok yöntemi öğreneceksiniz.
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Earn a skill badge by completing the Set Up a Google Cloud Network skill badge course, where you will learn how to perform basic networking tasks on Google Cloud Platform - create a custom network, add subnets firewall rules, then create VMs and test the latency when they communicate with each other.
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Earn a skill badge by completing the Share Data Using Google Data Cloud skill badge course, where you will gain practical experience with Google Cloud Data Sharing Partners, which have proprietary datasets that customers can use for their analytics use cases. Customers subscribe to this data, query it within their own platform, then augment it with their own datasets and use their visualization tools for their customer facing dashboards.
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Complete the 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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Giriş düzeyindeki BigQuery Verilerinden Analiz Elde Etme beceri rozetini alarak şu konulardaki becerilerinizi gösterin: SQL sorguları yazma, herkese açık tabloları sorgulama, örnek verileri BigQuery'ye yükleme, BigQuery'deki sorgu doğrulayıcı ile yaygın söz dizimi sorunlarını giderme ve BigQuery verilerine bağlanarak Data Studio'da rapor oluşturma.
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This course covers building ML models with TensorFlow and Keras, improving the accuracy of ML models and writing ML models for scaled use.
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The course begins with a discussion about data: how to improve data quality and perform exploratory data analysis. We describe Vertex AI AutoML and how to build, train, and deploy an ML model without writing a single line of code. You will understand the benefits of Big Query ML. We then discuss how to optimize a machine learning (ML) model and how generalization and sampling can help assess the quality of ML models for custom training.
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This course explores what ML is and what problems it can solve. The course also discusses best practices for implementing machine learning. You’re introduced to Vertex AI, a unified platform to quickly build, train, and deploy AutoML machine learning models.
The course discusses the five phases of converting a candidate use case to be driven by machine learning, and why it’s important to not skip them. The course ends with recognizing the biases that ML can amplify and how to recognize them.
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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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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 introductory Create and Manage Cloud SQL for PostgreSQL Instances skill badge to demonstrate skills in the following: migrating, configuring, and managing Cloud SQL for PostgreSQL instances and databases.
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Cloud SQL is a fully managed database service that stands out from its peers due to high performance, seamless integration, and impressive scalability. In this quest you will receive hands-on practice with the basics of Cloud SQL and quickly progress to advanced features, which you will apply to production frameworks and application environments. From creating instances and querying data with SQL, to building Deployment Manager scripts and connecting Cloud SQL instances with applications run on GKE containers, this quest will give you the knowledge and experience needed so you can start integrating this service right away.
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Complete the intermediate Develop Serverless Apps with Firebase skill badge course to demonstrate skills in the following: architecting and building serverless web applications with Firebase, utilizing Firestore for database management, automating deployment processes using Cloud Build, and integrating Google Assistant functionality into your applications.
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Güvenli Bir Google Cloud Ağı Oluşturma kursunu tamamlayarak beceri rozeti kazanın. Bu kursta, Google Cloud'da uygulamalarınızı derlemek, ölçeklendirmek ve korumak için ağla ilgili birden fazla kaynak hakkında bilgi edineceksiniz.
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In this course, you learn how to design APIs, and how to use OpenAPI specifications to document them. You learn about the API lifecycle, and how the Apigee API platform helps you manage all aspects of the lifecycle. You learn how APIs can be designed using API proxies, and how APIs are packaged as API products to be used by app developers.
Through a combination of lessons, hands-on labs, and supplemental materials, you will learn how to design, build, secure, deploy, and manage API solutions using Google Cloud's Apigee API Platform. This is the first course of the Developing APIs with the Apigee API Platform series. After completing this course, enroll in the API Security with the Apigee API Platform course.
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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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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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This course provides an introduction to using Terraform for Google Cloud. It enables learners to describe how Terraform can be used to implement infrastructure as code and to apply some of its key features and functionalities to create and manage Google Cloud infrastructure.
Learners will get hands-on practice building and managing Google Cloud resources using Terraform.
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Complete the intermediate Optimize Costs for Google Kubernetes Engine skill badge course to demonstrate skills in the following: creating and managing multi-tenant clusters, monitoring resource usage by namespace, configuring cluster and pod autoscaling for efficiency, setting up load balancing for optimal resource distribution, and implementing liveness and readiness probes to ensure application health and cost-effectiveness.
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Google Cloud'da Web Sitesi Derleme beceri rozeti kursunu tamamlayarak giriş düzeyindeki beceri rozetini kazanın. Get Cooking in Cloud serisine dayanan bu kursta şu konular ele alınır:Cloud Run'da web sitesi dağıtmaCompute Engine'de web uygulaması barındırmaGoogle Kubernetes Engine'de web sitenizi oluşturma, dağıtma ve ölçeklendirmeCloud Build'i kullanarak monolitik bir uygulamadan mikro hizmetler mimarisine geçiş
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Google Cloud'da Uygulama Geliştirme Ortamı Oluşturma kursunu tamamlayarak beceri rozeti kazanın. Bu kursta Cloud Storage, Identity and Access Management, Cloud Functions ve Pub/Sub gibi teknolojilerin temel özelliklerini kullanarak depolama odaklı bulut altyapısı oluşturma ve bu altyapıyla bağlantı kurmayı öğreneceksiniz.
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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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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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Giriş düzeyindeki Compute Engine İçin Cloud Load Balancing'i Uygulama beceri rozetini tamamlayarak şu konulardaki becerilerinizi gösterin: Compute Engine'de sanal makineler oluşturma ve dağıtma. Ağ ve uygulama yük dengeleyicileri yapılandırma.
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Google Cloud Fundamentals: Core Infrastructure introduces important concepts and terminology for working with Google Cloud. Through videos and hands-on labs, this course presents and compares many of Google Cloud's computing and storage services, along with important resource and policy management tools.
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Bu giriş düzeyindeki kursta, Google Cloud'un temel araçları ve hizmetleri hakkında uygulamalı alıştırmalar yapacaksınız. Laboratuvarlarda işlenen kavramlara ilişkin daha fazla bağlam bilgisi ve inceleme sunmak amacıyla isteğe bağlı videolar paylaşılmaktadır. Google Cloud'un Temel Özellikleri, Google Cloud'u kullanmayı öğrenenler için önerilen ilk kurslardandır. Bulut teknolojisi konusunda çok az bilginiz olsa ya da hiç bilginiz olmasa bile bu kursa katılabilir ve ilk Google Cloud projenizde uygulayabileceğiniz pratik deneyimler kazanabilirsiniz. Cloud Shell komutları yazmak ve ilk sanal makinenizi dağıtmaktan Kubernetes Engine üzerinde veya yük dengelemeyi kullanarak uygulama çalıştırmaya kadar birçok konuyu kapsayan Google Cloud'un Temel Özellikleri, platformun temel özelliklerine dair mükemmel bir giriş niteliğindedir.
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