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Apply your skills in Google Cloud console

SaiBabu Polisetti

Учасник із 2025

Срібна ліга

Кількість балів: 3078
Streaming Analytics into BigQuery Earned груд. 12, 2025 EST
Create a Secure Data Lake on Cloud Storage Earned груд. 12, 2025 EST
Get Started with Sensitive Data Protection Earned груд. 12, 2025 EST
Manage Kubernetes in Google Cloud Earned груд. 12, 2025 EST
Store, Process, and Manage Data on Google Cloud - Command Line Earned груд. 12, 2025 EST
Налаштування мережі Google Cloud Earned груд. 12, 2025 EST
Develop Serverless Apps with Firebase Earned груд. 12, 2025 EST
Develop Serverless Applications on Cloud Run Earned груд. 12, 2025 EST
Build Serverless Applications with Cloud Run Functions Earned груд. 11, 2025 EST
Build a Secure Google Cloud Network Earned груд. 11, 2025 EST
Share Data Using Google Data Cloud Earned груд. 10, 2025 EST
Implement CI/CD Pipelines on Google Cloud Earned груд. 9, 2025 EST
Automate Data Capture at Scale with Document AI Earned груд. 9, 2025 EST
Підготовка даних для інтерфейсів API машинного навчання в Google Cloud Earned груд. 9, 2025 EST
Create ML Models with BigQuery ML Earned груд. 9, 2025 EST
Boost Productivity with Gemini in BigQuery Earned груд. 7, 2025 EST
Work with Gemini Models in BigQuery Earned груд. 6, 2025 EST
Using BigQuery Machine Learning for Inference Earned груд. 6, 2025 EST
Gemini for Data Scientists and Analysts Earned груд. 5, 2025 EST

Earn a skill badge by completing the Streaming Analytics into BigQuery skill badge course, where you use Pub/Sub, Dataflow and BigQuery together to stream data for analytics.

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Complete the introductory Create a Secure Data Lake on Cloud Storage skill badge course to demonstrate skills in the following: securing and configuring a Cloud Storage bucket, using Gemini for text generation, managing IAM access control, and establishing a Knowledge Catalog lake for data governance.

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Complete the introductory Get Started with Sensitive Data Protection 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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Complete the intermediate Manage Kubernetes in Google Cloud skill badge course to demonstrate skills in the following: managing deployments with kubectl, monitoring and debugging applications on Google Kubernetes Engine (GKE), and continuous delivery techniques.

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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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Щоб отримати кваліфікаційний значок, пройдіть курс Налаштування мережі Google Cloud. У ньому ви дізнаєтеся про різні способи розгортання й моніторингу додатків, зокрема навчитеся визначати ролі керування ідентифікацією і доступом, надавати або вилучати доступ до проектів, створювати мережі VPC, розгортати й відстежувати віртуальні машини Compute Engine, писати запити SQL, а також по-різному вводити додатки в дію за допомогою Kubernetes.

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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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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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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 Build a Secure Google Cloud Network skill badge course, where you will learn about multiple networking-related resources to build, scale, and secure your applications on Google Cloud.

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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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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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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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Пройдіть вступний кваліфікаційний курс Підготовка даних для інтерфейсів API машинного навчання в Google Cloud, щоб продемонструвати свої навички щодо очистки даних за допомогою сервісу Dataprep by Trifacta, запуску конвеєрів даних у Dataflow, створення кластерів і запуску завдань Apache Spark у Managed Service for Apache Spark, а також виклику API машинного навчання, зокрема Cloud Natural Language API, Google Cloud Speech-to-Text API і Video Intelligence API.

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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. Duet AI was renamed to Gemini, our next-generation model.

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