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Aditya Yadav

Member since 2025

Gold League

3365 points
Arcade Adventure: Google Cloud ML APIs Earned Eki 22, 2026 EDT
Analyze Images with the Cloud Vision API Earned Eki 17, 2026 EDT
Arcade Trail: Data Engineering and Information Protection Earned Haz 18, 2026 EDT
Build a Certification Study Guide: ACE Exam Prep Earned Haz 6, 2026 EDT
Compute Engine İçin Cloud Load Balancing'i Uygulama Earned Haz 6, 2026 EDT
Implement Sensitive Data Protection on Google Cloud Earned Haz 6, 2026 EDT
Google Cloud'da Makine Öğrenimi API'leri İçin Veri Hazırlama Earned Haz 6, 2026 EDT
Geliştiriciler İçin Sorumlu Yapay Zeka: Adalet ve Önyargı Earned Haz 4, 2026 EDT
Implement Cloud Collaboration and Productivity Workflows Earned Haz 4, 2026 EDT
Machine Learning Operations (MLOps) with Agent Platform: Model Evaluation Earned Haz 2, 2026 EDT
Üretken Yapay Zeka İçin Makine Öğrenimi Operasyonları (MLOps) Earned Haz 2, 2026 EDT

Teaching computers to see and listen is a lot easier when you have pre-trained machine learning APIs doing the heavy lifting. In this adventure, you'll start by querying Google Cloud endpoints directly from APIs Explorer, then teach your applications to read multilingual text, recognize facial features, and spot landmarks in raw photos. Once your visual pipeline is dialed in, you'll turn to audio—converting spoken words into real-time transcripts and streaming live audio directly through web interfaces. Knock out both challenge labs unassisted to prove you can turn unstructured sight and sound into clean, actionable cloud intelligence.

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Earn a skill badge by completing the Analyze Images with the Cloud Vision API quest, where you discover how to leverage the Cloud Vision API for various tasks, including extracting text from images.

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Raw data is only useful if it is clean, structured, and secure. In this track, you’ll start by processing and preparing data using Dataprep, Dataflow templates, and Apache Spark. You'll learn how to clean up datasets so they are ready for machine learning models. From there, you'll focus on security by working with tools that find and mask sensitive information like personal IDs and credentials. You'll practice redacting critical details and creating safe, de-identified copies of your data in Cloud Storage. By finishing the hands-on challenge labs, you’ll show you can handle big data workflows while keeping confidential information completely safe.

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

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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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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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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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Bu kursta, sorumlu yapay zeka kavramı ve yapay zeka ilkeleri tanıtılmaktadır. Kurs, adalet ve önyargıyı pratik şekilde tanımlama teknikleri ile yapay zeka/makine öğrenimi uygulamalarında önyargının azaltılması konularını ele almaktadır. Kurs boyunca, Google Cloud ürünleri ve açık kaynaklı araçları kullanarak sorumlu yapay zekayla ilgili en iyi uygulamaları benimsemenize yardımcı olacak pratik yöntemler ve araçları tanıyacaksınız.

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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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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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Bu kurs, MLOps ekiplerinin üretken yapay zeka modellerini dağıtırken ve yönetirken karşılaştığı zorlukların üstesinden gelmek için gereken bilgi ve araçları sağlamaktadır. Ayrıca yapay zeka ekiplerinin, MLOps süreçlerini kolaylaştırıp üretken yapay zeka projelerinde başarıya ulaşması için Vertex AI'ın nasıl yardımcı olduğunu öğrenmenizi amaçlamaktadır.

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