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Aditya Yadav

Membro dal giorno 2025

Campionato Oro

3365 punti
Arcade Adventure: Google Cloud ML APIs Earned ott 22, 2026 EDT
Analyze Images with the Cloud Vision API Earned ott 17, 2026 EDT
Arcade Trail: Data Engineering and Information Protection Earned giu 18, 2026 EDT
Build a Certification Study Guide: ACE Exam Prep Earned giu 6, 2026 EDT
Implementazione di Cloud Load Balancing per Compute Engine Earned giu 6, 2026 EDT
Implement Sensitive Data Protection on Google Cloud Earned giu 6, 2026 EDT
Prepara i dati per le API ML su Google Cloud Earned giu 6, 2026 EDT
AI responsabile per sviluppatori: equità e bias Earned giu 4, 2026 EDT
Implement Cloud Collaboration and Productivity Workflows Earned giu 4, 2026 EDT
Machine Learning Operations (MLOps) with Agent Platform: Model Evaluation Earned giu 2, 2026 EDT
Machine Learning Operations (MLOps) for Generative AI Earned giu 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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Completa il corso introduttivo con badge delle competenze Implementazione di Cloud Load Balancing per Compute Engine per dimostrare le tue competenze nei seguenti ambiti: creazione ed esecuzione del deployment di macchine virtuali in Compute Engine e configurazione di bilanciatori del carico di rete e delle applicazioni.

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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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Ottieni il corso intermedio con badge delle competenze Prepara i dati per le API ML su Google Cloud per dimostrare le tue competenze nei seguenti ambiti: pulizia dei dati con Dataprep di Trifacta, esecuzione delle pipeline di dati in Dataflow, creazione dei cluster ed esecuzione dei job Apache Spark in Managed Service for Apache Spark e richiamo delle API ML tra cui l'API Cloud Natural Language, l'API Google Cloud Speech-to-Text e l'API Video Intelligence.

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Questo corso introduce i concetti di AI responsabile e i principi dell'AI. Tratta le tecniche per identificare sostanzialmente l'equità e i bias e mitigare i bias nelle pratiche di AI/ML. Illustra metodi e strumenti pratici per implementare le best practice dell'AI responsabile utilizzando gli strumenti open source e i prodotti Google Cloud.

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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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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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