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C Binduvardhana Reddy

Member since 2022

Gold League

20975 points
Üretken Yapay Zekaya Giriş Earned Nis 17, 2024 EDT
MySQL to Cloud Spanner Earned Oca 31, 2024 EST
Sorumlu Yapay Zeka'ya Giriş Earned Oca 31, 2024 EST
Büyük Dil Modellerine Giriş Earned Oca 31, 2024 EST
Sorumlu Yapay Zeka: Google Cloud ile Yapay Zeka İlkelerinin Uygulanması Earned Oca 31, 2024 EST
Gemini in Google Meet Earned Oca 29, 2024 EST
Gemini in Google Sheets Earned Oca 29, 2024 EST
Google Backup and Disaster Recovery Earned Oca 26, 2024 EST
Modernization of Applications using Openshift on Google Cloud Earned Oca 25, 2024 EST
Gemini in Google Docs Earned Oca 24, 2024 EST
Document AI: Building a Custom Document Extractor Earned Oca 24, 2024 EST
Google API products: A key to modern application development Earned Oca 24, 2024 EST
.NET Modernization Earned Oca 22, 2024 EST
Migrating Hadoop Workloads Earned Oca 22, 2024 EST
Migrating Pivotal Cloud Foundry (PCF) to Google Cloud Earned Oca 19, 2024 EST
Gemini in Google Slides Earned Oca 18, 2024 EST
Gemini in Gmail Earned Oca 18, 2024 EST
Introduction to Google Workspace with Gemini Earned Oca 18, 2024 EST
Azure Virtual Machines to Compute Engine Earned Oca 18, 2024 EST
Cloudera to Google Cloud Earned Oca 15, 2024 EST
AWS EC2 to Compute Engine Earned Oca 15, 2024 EST
Modernization of Java Applications for Google Cloud Earned Oca 15, 2024 EST
Microsoft SQL Server to Cloud SQL Earned Oca 10, 2024 EST
Migrating Oracle Workloads to Google Cloud Earned Oca 10, 2024 EST
On-Premises VMware to Compute Engine Earned Oca 4, 2024 EST
VM Migration for Partners Earned Oca 4, 2024 EST
MySQL to Cloud SQL Earned Oca 4, 2024 EST
Google Cloud Big Data and Machine Learning Fundamentals Earned Oca 4, 2024 EST

Bu, üretken yapay zekanın ne olduğunu, nasıl kullanıldığını ve geleneksel makine öğrenme yöntemlerinden nasıl farklı olduğunu açıklamayı amaçlayan giriş seviyesi bir mikro öğrenme kursudur. Ayrıca kendi üretken yapay zeka uygulamalarınızı geliştirmenize yardımcı olacak Google Araçlarını da kapsar.

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Migration from MySQL to Cloud Spanner using Dataflow that includes sample mock data and all necessary steps with initial assessment to validation including taking care of migrating users and grants.

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Bu kurs, sorumlu yapay zekanın ne olduğunu, neden önemli olduğunu ve Google'ın sorumlu yapay zekayı ürünlerinde nasıl uyguladığını açıklamayı amaçlayan giriş seviyesinde bir mikro öğrenme kursudur. Ayrıca Google'ın 7 yapay zeka ilkesini de tanıtır.

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Bu giriş seviyesi mikro öğrenme kursunda büyük dil modelleri (BDM) nedir, hangi kullanım durumlarında kullanılabileceği ve büyük dil modelleri performansını artırmak için nasıl istem ayarlaması yapabileceğiniz keşfedilecektir. Ayrıca kendi üretken yapay zeka uygulamalarınızı geliştirmenize yardımcı olacak Google araçları hakkında bilgi verilecektir.

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Kurumsal yapay zeka ve makine öğreniminin kullanımı artmaya devam ettikçe, bunu sorumlu bir şekilde oluşturmanın önemi de artıyor. Sorumlu yapay zeka hakkında konuşmanın, onu uygulamaya koymaktan çok daha kolay olabilmesi burada bir zorluk oluşturmaktadır. Kuruluşunuzda sorumlu yapay zekayı nasıl işlevsel hale getireceğinizi öğrenmekle ilgileniyorsanız, bu kurs tam size göre. Bu kurs, Google Cloud'un sorumlu yapay zeka yaklaşımını nasıl uyguladığını derinlemesine inceleyerek, kendi sorumlu yapay zeka stratejinizi oluşturmanız için size kapsamlı bir çerçeve sunuyor.

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Google Workspace with Gemini provides customers with access to generative AI features. This course delves into the capabilities of Gemini in Google Meet. Through video lessons, hands-on activities and practical examples, you will gain a comprehensive understanding of the Gemini features in Google Meet. You learn how to use Gemini to generate background images, improve your video quality, and translate captions. By the end of this course, you'll be equipped with the knowledge and skills to confidently utilize Gemini in Google Meet to maximize the effectiveness of your video conferences.

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Google Workspace with Gemini provides customers with generative AI features in Google Workspace. In this mini-course, you learn about the key features of Gemini and how they can be used to improve productivity and efficiency in Google Sheets.

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This workload aims to upskill Google Cloud partners to deploy and manage Google Backup and Disaster Recovery (BDR). The following will be addressed: the core components and business value of Google BDR, the prerequisites before installing Google BDR, the initial deployment of Google BDR, creating and configuring components of a Backup Plan, the components of a Backup Plan, discovering VMware and Compute Engine VMs, and protecting, backing up, and restoring VMs.

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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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Google Workspace with Gemini provides customers with access to generative AI features. This course delves into the capabilities of Gemini in Google Docs using video lessons, hands-on activities and practical examples. You learn how to use Gemini to generate written content based on prompts. You also explore using Gemini to edit text you’ve already written, helping you improve your overall productivity. By the end of this course, you'll be equipped with the knowledge and skills to confidently utilize Gemini in Google Docs to improve your writing.

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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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Outline the key steps in publishing an API to deliver selective company information to applications created by external developers.

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This learning path aims to upskill Google Cloud partners to perform the specific tasks associated with the priority workload. Learners will discover the specific tasks in rehosting applications from on-premises to Google Cloud. It also aims to re-platform applications to run in GKE. Learners will perform the tasks of Migrating MySQL, Angular, and .NET applications from their on-premises machines to Google Cloud VM instances. Sample code will be used during the migration. Learners will complete a challenge lab that focuses on the critical steps in a rehosting exercise - copying over code for the back-end, front-end, and middle-tier applications and validating that the applications have been migrated correctly. Learners will also complete a challenge lab that focuses on the critical steps in a re-platforming exercise - creating back-end, front-end, and middle-tier Docker images, deploying the same in the GKE cluster, and validating that the application has been deployed correctly.

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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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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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Google Workspace with Gemini provides customers with generative AI features in Google Workspace. In this mini-course, you learn about the key features of Gemini and how they can be used to improve productivity and efficiency in Google Slides.

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Google Workspace with Gemini provides customers with generative AI features in Google Workspace. In this mini-course, you learn about the key features of Gemini and how they can be used to improve productivity and efficiency in Gmail.

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Google Workspace with Gemini provides customers with generative AI features in Google Workspace. In this learning path, you learn about the key features of Gemini and how they can be used to improve productivity and efficiency in Google Workspace.

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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 workload aims to upskill Google Cloud partners to perform specific tasks associated with priority workloads. Learners will perform the tasks of migrating data from five products hosted on Cloudera or Hortonworks to corresponding Google Cloud services and hosted products. The migration solutions addressed will be: HDFS data to Google Cloud Dataproc and Cloud Storage Hive data to Cloud Dataproc and the Cloud Dataproc Metastore Hive data to Google Cloud BigQuery Impala data to Google Cloud BigQuery HBase to Google Cloud Bigtable Sample data will be used during all five migrations. 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.

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Migration from AWS EC2 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 aims to upskill Google Cloud partners to perform specific tasks in rehosting applications from on-premise to Google Cloud. It also aims to re-platform applications to run in GKE. Learners will perform the tasks of Migrating MySQL, Angular, and Java applications from their on-premise machines to Google Cloud VM instances. Sample code will be used during the migration.

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This course aims to upskill Google Cloud partners to perform specific tasks of migrating data from Microsoft SQL Server to CloudSQL using the built-in replication capabilities of SQL Server. 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. One or more challenge labs will test the learner's understanding of the topics.

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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 rehosting Oracle Workloads on Google Cloud.

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Migration from on-premises VMware 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 provides comprehensive skills on VM migration, from the initial assessment through the final implementation through presentations, demonstrations, and whiteboard session.

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Migration from MySQL to Cloud SQL using Database Migration Service that includes sample mock data and all necessary steps with initial assessment to validation including taking care of migrating users and grants.

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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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