Poojasree Balasubramanian
Membro dal giorno 2025
Campionato Oro
7180 punti
[DEPRECATED] Gemini for end-to-end SDLC
Earned gen 16, 2026 EST
AI responsabile per sviluppatori: privacy e sicurezza
Earned gen 14, 2026 EST
AI responsabile per sviluppatori: interpretabilità e trasparenza
Earned gen 14, 2026 EST
Model Armor: Securing AI Deployments
Earned gen 13, 2026 EST
Introduction to Security in the World of AI
Earned gen 12, 2026 EST
AI responsabile per sviluppatori: equità e bias
Earned gen 11, 2026 EST
Machine Learning Operations (MLOps) with Agent Platform: Model Evaluation
Earned gen 11, 2026 EST
Machine Learning Operations (MLOps) for Generative AI
Earned gen 11, 2026 EST
Create ML Models with BigQuery ML
Earned gen 11, 2026 EST
Boost Productivity with Gemini in BigQuery
Earned gen 8, 2026 EST
Work with Gemini Models in BigQuery
Earned dic 30, 2025 EST
Using BigQuery Machine Learning for Inference
Earned dic 28, 2025 EST
[DEPRECATED] Gemini for Data Scientists and Analysts
Earned dic 27, 2025 EST
Analyze Speech and Language with Google APIs
Earned nov 18, 2025 EST
Monitoring in Google Cloud
Earned nov 18, 2025 EST
Build Serverless Applications with Cloud Run Functions
Earned nov 18, 2025 EST
Store, Process, and Manage Data on Google Cloud - Console
Earned nov 18, 2025 EST
Set Up a Google Cloud Network
Earned nov 18, 2025 EST
Implement Cloud Collaboration and Productivity Workflows
Earned nov 17, 2025 EST
Organize and Govern Data with Knowledge Catalog
Earned nov 17, 2025 EST
[Deprecated] Get Started with Looker
Earned nov 17, 2025 EST
Level 3: Scalable Deployments and Delivery
Earned nov 13, 2025 EST
Deploy and Secure Serverless APIs with API Gateway
Earned nov 5, 2025 EST
Implement Event-Driven Messaging and Automation Workflows
Earned nov 3, 2025 EST
Implement Cloud Storage and Data Protection Solutions
Earned nov 1, 2025 EDT
The Basics of Google Cloud Compute
Earned ott 28, 2025 EDT
In this course, you learn how Gemini, a generative AI-powered collaborator from Google Cloud, helps you use Google products and services to develop, test, deploy, and manage applications. With help from Gemini, you learn how to develop and build a web application, fix errors in the application, develop tests, and query data. Using a hands-on lab, you experience how Gemini improves the software development lifecycle (SDLC).
Duet AI was renamed to Gemini, our next-generation model.
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Questo corso introduce argomenti importanti relativi alla privacy e alla sicurezza dell'AI. Esplora metodi e strumenti pratici per implementare le pratiche consigliate per la privacy e la sicurezza dell'AI utilizzando gli strumenti open source e i prodotti Google Cloud.
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Questo corso introduce i concetti di interpretabilità e la trasparenza dell'AI. Parla dell'importanza della trasparenza dell'AI per sviluppatori ed engineer. Illustra metodi e strumenti pratici per aiutare a raggiungere interpretabilità e trasparenza sia nei dati che nei modelli di AI.
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This course reviews the essential security features of Model Armor and equips you to work with the service. You’ll learn about the security risks associated with LLMs and how Model Armor protects your AI apps. Explore other content in the Gemini Agent Ready (GEAR) program.
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Artificial Intelligence (AI) offers transformative possibilities, but it also introduces new security challenges. This course equips security and data protection leaders with strategies to securely manage AI within their organizations. Learn a framework for proactively identifying and mitigating AI-specific risks, protecting sensitive data, ensuring compliance, and building a resilient AI infrastructure. Pick use cases from four different industries to explore how these strategies apply in real-world scenarios.
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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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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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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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This course explores Gemini in BigQuery, a suite of AI-driven features to assist data-to-AI workflow. These features include data exploration and preparation, code generation and troubleshooting, and workflow discovery and visualization. Through conceptual explanations, a practical use case, and hands-on labs, the course empowers data practitioners to boost their productivity and expedite the development pipeline.
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This course demonstrates how to use AI/ML models for generative AI tasks in BigQuery. Through a practical use case involving customer relationship management, you learn the workflow of solving a business problem with Gemini models. To facilitate comprehension, the course also provides step-by-step guidance through coding solutions using both SQL queries and Python notebooks.
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Learn about BigQuery ML for Inference, why Data Analysts should use it, its use cases, and supported ML models. You will also learn how to create and manage these ML models in BigQuery.
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In this course, you learn how Gemini, a generative AI-powered collaborator from Google Cloud, helps analyze customer data and predict product sales. You also learn how to identify, categorize, and develop new customers using customer data in BigQuery. Using hands-on labs, you experience how Gemini improves data analysis and machine learning workflows.
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Earn a skill badge by completing the Analyze Speech and Language with Google APIs quest, where you learn how to use the Natural Language and Speech APIs in real-world settings.
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Complete the introductory Monitoring in Google Cloud skill badge course to demonstrate skills in the following: using Cloud Monitoring tools to monitor resources on Google Cloud.
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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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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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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 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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Complete the introductory Organize and Govern Data with Knowledge Catalog skill badge to demonstrate skills in the following: creating Knowledge Catalog assets, creating aspect types, and applying aspects to entries in Knowledge Catalog.
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Earn a skill badge by completing the Get Started with Looker skill badge course, where you learn how to analyze, visualize, and curate data using Looker Studio and Looker.
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From code to cloud, automation drives reliable, repeatable deployment. With over 80% of enterprises adopting DevOps practices, this challenge will guide you through building and managing secure, scalable infrastructure on Google Cloud. You’ll create Artifact Registries for Go, Python, Maven, and more—managing dependencies securely—and deploy static sites using Nginx, Traefik, and Caddy V2 on Cloud Run. Then you will dive into into Terraform Essentials, where you’ll define cloud resources as code—from VPCs and firewall rules to Compute Engine and Cloud Storage—and safeguard configurations with Secret Manager. Together, these labs give you hands-on experience in automating deployments and strengthening cloud infrastructure from the ground up.
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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 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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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 The Basics of Google Cloud Compute skill badge course, where you learn how to work with virtual machines (VMs), persistent disks, and web servers using Compute Engine.
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