Xueqian Pek
Jest członkiem od 2020
Liga srebrna
24300 pkt.
Deprecated : Managing Machine Learning Projects with Google Cloud
Earned kwi 30, 2022 EDT
Managing Cloud Infrastructure with Terraform
Earned kwi 29, 2022 EDT
Scaling with Google Cloud Operations
Earned kwi 24, 2022 EDT
Modernize Infrastructure and Applications with Google Cloud
Earned kwi 24, 2022 EDT
[DEPRECATED] Google Cloud Big Data and Machine Learning Fundamentals
Earned kwi 24, 2022 EDT
Manage Security in Google Cloud
Earned kwi 23, 2022 EDT
[DEPRECATED] Google Cloud Computing Foundations: Infrastructure in Google Cloud - Locales
Earned kwi 22, 2022 EDT
Google Cloud Computing Foundations: Networking & Security in Google Cloud
Earned kwi 22, 2022 EDT
Google Cloud Computing Foundations: Data, ML, and AI in Google Cloud
Earned kwi 22, 2022 EDT
DEPRECATED Developing Data and Machine Learning Apps with C#
Earned kwi 22, 2022 EDT
Budowanie bezpiecznej sieci Google Cloud
Earned kwi 21, 2022 EDT
Security & Identity Fundamentals
Earned kwi 18, 2022 EDT
Google Cloud Solutions I: Scaling Your Infrastructure
Earned kwi 14, 2022 EDT
Build Infrastructure with Terraform on Google Cloud
Earned kwi 10, 2022 EDT
Automate Interactions with Contact Center AI
Earned lis 13, 2021 EST
Prepare Data for Looker Dashboards and Reports
Earned lis 12, 2021 EST
Applying Advanced LookML Concepts in Looker
Earned lis 11, 2021 EST
Data Science on Google Cloud
Earned lis 11, 2021 EST
Cloud SQL
Earned lis 10, 2021 EST
Scientific Data Processing
Earned lis 9, 2021 EST
Migrate MySQL Data to Cloud SQL Using Database Migration Service
Earned lis 8, 2021 EST
Migrating MySQL data to Cloud SQL using Database Migration Service
Earned lis 7, 2021 EST
Build a Data Warehouse with BigQuery
Earned lis 6, 2021 EDT
[Deprecated] Data Science on Google Cloud: Machine Learning
Earned lis 5, 2021 EDT
DEPRECATED Explore Machine Learning Models with Explainable AI
Earned lis 4, 2021 EDT
Understanding LookML in Looker
Earned lis 4, 2021 EDT
Create ML Models with BigQuery ML
Earned lis 1, 2021 EDT
DEPRECATED Applying BigQuery ML's Classification, Regression, and Demand Forecasting for Retail Applications
Earned paź 29, 2021 EDT
Anthos: Service Mesh
Earned mar 16, 2021 EDT
Monitor and Log with Google Cloud Observability
Earned mar 16, 2021 EDT
Implement DevOps Workflows in Google Cloud
Earned mar 15, 2021 EDT
[DEPRECATED] Build Interactive Apps with Google Assistant
Earned mar 14, 2021 EDT
Tworzenie witryny w Google Cloud
Earned mar 14, 2021 EDT
Getting Started with Go on Google Cloud
Earned mar 13, 2021 EST
Develop Serverless Applications on Cloud Run
Earned mar 13, 2021 EST
Budowanie sieci w Google Cloud
Earned mar 13, 2021 EST
Konfigurowanie środowiska programistycznego w Google Cloud
Earned mar 12, 2021 EST
Develop Serverless Apps with Firebase
Earned mar 12, 2021 EST
Deploy Kubernetes Applications on Google Cloud
Earned mar 5, 2021 EST
Cloud Architecture: Design, Implement, and Manage
Earned mar 5, 2021 EST
Wdrażanie równoważenia obciążenia Cloud Load Balancing w Compute Engine
Earned mar 1, 2021 EST
[Deprecated] Advanced ML: ML Infrastructure
Earned wrz 24, 2020 EDT
Cloud Healthcare API
Earned wrz 23, 2020 EDT
DEPRECATED BigQuery for Data Warehousing
Earned wrz 22, 2020 EDT
Data Catalog Fundamentals
Earned wrz 19, 2020 EDT
Engineer Data for Predictive Modeling with BigQuery ML
Earned wrz 17, 2020 EDT
Google Developer Essentials
Earned wrz 14, 2020 EDT
Uzyskiwanie statystyk z danych BigQuery
Earned wrz 14, 2020 EDT
Przygotowywanie danych do użycia z interfejsami ML w Google Cloud
Earned wrz 14, 2020 EDT
BigQuery for Machine Learning
Earned wrz 13, 2020 EDT
Use Machine Learning APIs on Google Cloud
Earned wrz 12, 2020 EDT
DEPRECATED Google Cloud Solutions II: Data and Machine Learning
Earned wrz 11, 2020 EDT
Intermediate ML: TensorFlow on Google Cloud
Earned wrz 11, 2020 EDT
VM Migration
Earned kwi 20, 2020 EDT
DEPRECATED BigQuery for Marketing Analysts
Earned kwi 19, 2020 EDT
Qwiklabs Challenge Speedrun
Earned kwi 19, 2020 EDT
[DEPRECATED] Data Engineering
Earned kwi 18, 2020 EDT
DEPRECATED BigQuery Basics for Data Analysts
Earned kwi 17, 2020 EDT
Google Cloud Run Serverless Workshop
Earned kwi 15, 2020 EDT
Cloud Development
Earned kwi 13, 2020 EDT
DEPRECATED Application Development - Java
Earned kwi 13, 2020 EDT
NCAA® March Madness®: Bracketology with Google Cloud
Earned kwi 12, 2020 EDT
Deprecated Kubernetes Solutions
Earned kwi 12, 2020 EDT
Baseline: Deploy & Develop
Earned kwi 11, 2020 EDT
Security on AWS
Earned kwi 9, 2020 EDT
DevOps Essentials
Earned kwi 9, 2020 EDT
Kubernetes in Google Cloud
Earned kwi 8, 2020 EDT
Cloud Architecture - Design, Implement, and Manage
Earned kwi 7, 2020 EDT
Inżynieria chmury
Earned kwi 3, 2020 EDT
DEPRECATED Cloud Architecture
Earned kwi 2, 2020 EDT
Podstawy: infrastruktura
Earned kwi 1, 2020 EDT
Podstawy: dane, uczenie maszynowe, AI
Earned kwi 1, 2020 EDT
Intro to ML: Language Processing
Earned mar 31, 2020 EDT
Podstawy Google Cloud
Earned mar 31, 2020 EDT
Machine Learning APIs
Earned mar 31, 2020 EDT
Intro to ML: Image Processing
Earned mar 30, 2020 EDT
Cloud Hero: Application Development
Earned mar 30, 2020 EDT
Cloud Hero: Big Data and Machine Learning
Earned mar 28, 2020 EDT
Cloud Hero: Core Infrastructure
Earned mar 27, 2020 EDT
Business professionals in non-technical roles have a unique opportunity to lead or influence machine learning projects. If you have questions about machine learning and want to understand how to use it, without the technical jargon, this course is for you. Learn how to translate business problems into machine learning use cases and vet them for feasibility and impact. Find out how you can discover unexpected use cases, recognize the phases of an ML project and considerations within each, and gain confidence to propose a custom ML use case to your team or leadership or translate the requirements to a technical team.
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In this Quest, the experienced user of Google Cloud will learn how to describe and launch cloud resources with Terraform, an open source tool that codifies APIs into declarative configuration files that can be shared amongst team members, treated as code, edited, reviewed, and versioned. In these nine hands-on labs, you will work with example templates and understand how to launch a range of configurations, from simple servers, through full load-balanced applications.
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Organizations of all sizes are embracing the power and flexibility of the cloud to transform how they operate. However, managing and scaling cloud resources effectively can be a complex task. This course explores the fundamental concepts of modern operations, reliability, and resilience in the cloud, and how Google Cloud can help support these efforts.
As part of the Cloud Digital Leader learning path, this course aims to help individuals grow in their role and build the future of their business.
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Many traditional enterprises use legacy systems and apps that can't stay up-to-date with modern customer expectations. Business leaders often have to choose between maintaining their aging IT systems and investing in new products and services. This course explores these challenges and offers solutions to overcome them by using cloud technology.
As part of the Cloud Digital Leader learning path, this course aims to help individuals grow in their role and build the future of their business.
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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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This course provides foundational knowledge for securing your Google Cloud environment. You will learn Google Cloud's security model, including Zero Trust principles and the Shared Fate model. You will configure identity services like Cloud Identity and Workforce Identity Federation, manage access control using Identity and Access Management (IAM) roles, conditions, and policies, and implement network security controls such as VPCs, Cloud Firewall tiers, and VPC Service Controls to build a secure perimeter.
As the foundation of the Security in Google Cloud learning path, this course establishes the core principles required for two subsequent specialized tracks: the five-course Security Best Practices in Google Cloud series and the four-course Mitigate Threats in Google Cloud series.
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Szkolenie Podstawy przetwarzania danych w Google Cloud pozwoli osobom z niewielkim
lub zerowym doświadczeniem z zakresu przetwarzania danych w chmurze szczegółowo
zapoznać się z najważniejszymi pojęciami z zakresu podstaw chmury, big data i systemów uczących się.
Zawiera także informacje o tym, gdzie i jak można wykorzystać Google Cloud.
Po zakończeniu szkolenia uczestnicy będą potrafili wyjaśnić pojęcia dotyczące przetwarzania danych w chmurze,
big data i systemów uczących się oraz zademonstrować wybrane umiejętności praktyczne.
To szkolenie należy do serii szkoleń o nazwie Google Cloud Computing Foundations (Podstawy usług w chmurze Google).
Szkolenia należy ukończyć w następującej kolejności:
Google Cloud Computing Foundations: Cloud Computing Fundamentals - Locales
Google Cloud Computing Foundations: Infrastructure in Google Cloud - Locales
Google Cloud Computing Foundations: Networking and Security in Google Cloud - Locales
Google Cloud Computing Foundation…
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The Google Cloud Computing Foundations courses are for individuals with little to no background or experience in cloud computing. They provide an overview of concepts central to cloud basics, big data, and machine learning, and where and how Google Cloud fits in.
By the end of the series of courses, learners will be able to articulate these concepts and demonstrate some hands-on skills.
The courses should be completed in the following order:
1. Google Cloud Computing Foundations: Cloud Computing Fundamentals
2. Google Cloud Computing Foundations: Infrastructure in Google Cloud
3. Google Cloud Computing Foundations: Networking and Security in Google Cloud
4. Google Cloud Computing Foundations: Data, ML, and AI in Google Cloud
This third course covers cloud automation and management tools and building secure networks.
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The Google Cloud Computing Foundations courses are for individuals with little to no background or experience in cloud computing. They provide an overview of concepts central to cloud basics, big data, and machine learning, and where and how Google Cloud fits in.
By the end of the series of courses, learners will be able to articulate these concepts and demonstrate some hands-on skills.
The courses should be completed in the following order:
1. Google Cloud Computing Foundations: Cloud Computing Fundamentals
2. Google Cloud Computing Foundations: Infrastructure in Google Cloud
3. Google Cloud Computing Foundations: Networking and Security in Google Cloud
4. Google Cloud Computing Foundations: Data, ML, and AI in Google Cloud
This final course in the series reviews managed big data services, machine learning and its value, and how to demonstrate your skill set in Google Cloud further by earning Skill Badges.
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C# has powered Windows .NET application development for nearly two decades and Google Cloud is committed to supporting developers getting their .NET workloads up and running on Google Cloud. In this quest, you will learn how to run C# apps in Google Cloud, and specifically how to take your apps to the next level by interfacing them with the big data and machine learning APIs that are accessible now from C#. By enrolling in this quest you will see firsthand how seamlessly Google Cloud integrates with .NET workloads and what the possibilities are for leveraging big data and ML services in your own C# projects.
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Aby zdobyć odznakę umiejętności, ukończ szkolenie Budowanie bezpiecznej sieci Google Cloud, w trakcie którego poznasz różne związane z siecią zasoby do budowania, skalowania i zabezpieczania aplikacji w Google Cloud.
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Security is an uncompromising feature of Google Cloud services, and Google Cloud has developed specific tools for ensuring safety and identity across your projects. In this fundamental-level quest, you will get hands-on practice with Google Cloud’s Identity and Access Management (IAM) service, which is the go-to for managing user and virtual machine accounts. You will get experience with network security by provisioning VPCs and VPNs, and learn what tools are available for security threat and data loss protections.
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In this course you will learn how you to harness serious Google Cloud power and infrastructure. The hands-on labs will give you use cases and you will be tasked with implementing scaling practices utilized by Google’s very own Solutions Architecture team. From developing enterprise grade load balancing and autoscaling, to building continuous delivery pipelines, Google Cloud Solutions I: Scaling your Infrastructure will teach you best practices for taking your Google Cloud projects to the next level.
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Complete the intermediate Build Infrastructure with Terraform on Google Cloud skill badge to demonstrate skills in the following: Infrastructure as Code (IaC) principles using Terraform, provisioning and managing Google Cloud resources with Terraform configurations, effective state management (local and remote), and modularizing Terraform code for reusability and organization.
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Earn a skill badge by completing the Automate Interactions with Contact Center AI quest, where you will learn about the features of Contact Center AI, including how to Build a virtual agent, Design conversation flows for your virtual agent; Add a phone gateway to your virtual agent; Use Dialogflow for troubleshooting; Review logs and debug your virtual agent. A skill badge is an exclusive digital badge issued by Google Cloud in recognition of your proficiency with Google Cloud products and services and tests your ability to apply your knowledge in an interactive hands-on environment. Complete the skill badge quest, and final assessment challenge lab, to receive a digital badge that you can share with your network.
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Complete the introductory Prepare Data for Looker Dashboards and Reports skill badge course to demonstrate skills in the following: filtering, sorting, and pivoting data; merging results from different Looker Explores; and using functions and operators to build Looker dashboards and reports for data analysis and visualization.
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In this course, you will get hands-on experience applying advanced LookML concepts in Looker. You will learn how to use Liquid to customize and create dynamic dimensions and measures, create dynamic SQL derived tables and customized native derived tables, and use extends to modularize your LookML code.
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This is the first of two Quests of hands-on labs is derived from the exercises from the book Data Science on Google Cloud Platform, 2nd Edition by Valliappa Lakshmanan, published by O'Reilly Media, Inc. In this first Quest, covering up through chapter 8, you are given the opportunity to practice all aspects of ingestion, preparation, processing, querying, exploring and visualizing data sets using Google Cloud tools and services.
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Cloud SQL is a fully managed database service that stands out from its peers due to high performance, seamless integration, and impressive scalability. In this quest you will receive hands-on practice with the basics of Cloud SQL and quickly progress to advanced features, which you will apply to production frameworks and application environments. From creating instances and querying data with SQL, to building Deployment Manager scripts and connecting Cloud SQL instances with applications run on GKE containers, this quest will give you the knowledge and experience needed so you can start integrating this service right away.
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Big data, machine learning, and scientific data? It sounds like the perfect match. In this course, you will get hands-on practice with GCP services like BigQuery, Managed Service for Apache Spark, and Tensorflow by applying them to use cases that employ real-life, scientific data sets. By getting experience with tasks like earthquake data analysis and satellite image aggregation, Scientific Data Processing will expand your skill set in big data and machine learning so you can start tackling your own problems across a spectrum of scientific disciplines.
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Complete the introductory Migrate MySQL Data to Cloud SQL Using Database Migration Service skill badge course to demonstrate skills in the following: migrating MySQL data to Cloud SQL using different job types and connectivity options available in Database Migration Service and migrating MySQL user data when running Database Migration Service jobs.
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This course offers hands-on practice with migrating MySQL data to Cloud SQL using Database Migration Service. You start with an introductory lab that briefly reviews how to get started with Cloud SQL for MySQL, including how to connect to Cloud SQL instances using the Cloud Console. Then, you continue with two labs focused on migrating MySQL databases to Cloud SQL using different job types and connectivity options available in Database Migration Service. The course ends with a lab on migrating MySQL user data when running Database Migration Service jobs.
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Complete the intermediate Build a Data Warehouse with BigQuery skill badge course to demonstrate skills in the following: joining data to create new tables, troubleshooting joins, appending data with unions, creating date-partitioned tables, and working with JSON, arrays, and structs in BigQuery.
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This is the second of two courses of hands-on labs derived from the exercises from the book Data Science on Google Cloud Platform, 2nd Edition by Valliappa Lakshmanan, published by O'Reilly Media, Inc. In this second Quest, covering chapter 9 through the end of the book, you extend the skills practiced in the first Quest, and run full-fledged machine learning jobs with state-of-the-art tools and real-world data sets, all using Google Cloud tools and services.
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Earn a skill badge by completing the Explore Machine Learning Models with Explainable AI quest, where you will learn how to do the following using Explainable AI: build and deploy a model to an AI platform for serving (prediction), use the What-If Tool with an image recognition model, identify bias in mortgage data using the What-If Tool, and compare models using the What-If Tool to identify potential bias. A skill badge is an exclusive digital badge issued by Google Cloud in recognition of your proficiency with Google Cloud products and services and tests your ability to apply your knowledge in an interactive hands-on environment. Complete this skill badge quest and the final assessment challenge lab to receive a skill badge that you can share with your network.
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In this course, you will get hands-on experience with LookML in Looker. You will learn how to write LookML code to create new dimensions and measures, create derived tables and join them to Explores, filter Explores, and define caching policies in LookML.
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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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In this course you will learn how to use several BigQuery ML features to improve retail use cases. Predict the demand for bike rentals in NYC with demand forecasting, and see how to use BigQuery ML for a classification task that predicts the likelihood of a website visitor making a purchase.
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This intermediate-level quest is unique among Qwiklabs quests. These labs have been curated to give operators hands-on practice with Anthos—a new, open application modernization platform on GCP. Anthos enables you to build and manage modern hybrid applications. Tasks include: installing service mesh, collecting telemetry, and securing your microservices with service mesh policies. This quest is composed of labs targeted to teach you everything you need to know to introduce service mesh, and Anthos, into your next hybrid cloud project.
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Complete the introductory Monitor and Log with Google Cloud Observability skill badge course to demonstrate skills in the following: monitoring virtual machines in Compute Engine, utilizing Cloud Monitoring for multi-project oversight, extending monitoring and logging capabilities to Cloud Functions, creating and sending custom application metrics, and configuring Cloud Monitoring alerts based on custom metrics.
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Complete the intermediate Implement DevOps Workflows in Google Cloud skill badge to demonstrate skills in the following: creating and managing Git repositories, launching, managing, and scaling deployments on Google Kubernetes Engine (GKE), and architecting CI/CD pipelines that automate container image builds and deployments to GKE.
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Earn a skill badge by completing the Build Interactive Apps with Google Assistant quest, where you will learn how to build Google Assistant applications, including how to: create an Actions project, integrate Dialogflow with an Actions project, test your application with Actions simulator, build an Assistant application with flash cards template, integrate customer MP3 files with your Assistant application, add Cloud Translation API to your Assistant application, and use APIs and integrate them into your applications. A skill badge is an exclusive digital badge issued by Google Cloud in recognition of your proficiency with Google Cloud products and services and tests your ability to apply your knowledge in an interactive hands-on environment. Complete the skill badge quest, and final assessment challenge lab, to receive a digital badge that you can share with your network.
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Aby zdobyć odznakę umiejętności potwierdzającą opanowanie podstaw, ukończ kurs wprowadzający Tworzenie witryny w Google Cloud. Jest on oparty na serii materiałów wideo Get Cooking in Cloud i obejmuje te tematy: Wdrażanie witryny w Cloud RunHostowanie aplikacji internetowej w Compute EngineTworzenie, wdrażanie i skalowanie witryny w Google Kubernetes EngineMigracja z aplikacji monolitycznej do architektury mikroserwisów przy użyciu Cloud Build
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Get started with Go (Golang) by reviewing Go code, and then creating and deploying simple Go apps on Google Cloud. Go is an open source programming language that makes it easy to build fast, reliable, and efficient software at scale. Go runs native on Google Cloud, and is fully supported on Google Kubernetes Engine, Compute Engine, App Engine, Cloud Run, and Cloud Functions. Go is a compiled language and is faster and more efficient than interpreted languages. As a result, Go requires no installed runtime like Node, Python, or JDK to execute.
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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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Aby zdobyć odznakę umiejętności, ukończ szkolenie Budowanie sieci w Google Cloud, w trakcie którego poznasz różne sposoby wdrażania i monitorowania aplikacji i dowiesz się, jak: przeglądać role uprawnień, dodawać/usuwać dostęp do projektu, tworzyć sieci VPC, wdrażać i monitorować maszyny wirtualne Compute Engine, pisać zapytania SQL oraz wdrażać aplikacje przy użyciu różnych metod w Kubernetes.
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Aby zdobyć odznakę umiejętności, ukończ szkolenie Konfigurowanie środowiska programistycznego w Google Cloud, w trakcie którego dowiesz się, jak utworzyć i podłączyć infrastrukturę w chmurzę do przechowywania danych przy użyciu podstawowych funkcji technologii Cloud Storage, Identity and Access Management, Cloud Functions oraz Pub/Sub.
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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 Deploy Kubernetes Applications on Google Cloud skill badge course to demonstrate skills in the following: Configuring and building Docker container images.Creating and managing Google Kubernetes Engine (GKE) clusters.Utilizing kubectl for efficient cluster management.Deploying Kubernetes applications with robust continuous delivery (CD) practices.
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Earn a skill badge by completing the Cloud Architecture: Design, Implement, and Manage to demonstrate skills in the following: deploy a publicly accessible website using Apache web servers, configure a Compute Engine VM using startup scripts, configure secure RDP using a Windows Bastion host and firewall rules, build and deploy a Docker image to a Kubernetes cluster and then update it, and create a CloudSQL instance and import a MySQL database. This skill badge is a great resource for understanding topics that will appear in the Google Cloud Certified Professional Cloud Architect certification exam.
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Ukończ szkolenie wprowadzające Wdrażanie równoważenia obciążenia Cloud Load Balancing w Compute Engine, aby zdobyć odznakę potwierdzającą zdobycie następujących umiejętności: tworzenie i wdrażanie maszyn wirtualnych w Compute Engine oraz konfigurowanie sieciowych systemów równoważenia obciążenia i systemów równoważenia obciążenia aplikacji.
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Machine Learning is one of the most innovative fields in technology, and the Google Cloud Platform has been instrumental in furthering its development. With a host of APIs, Google Cloud has a tool for just about any machine learning job. In this advanced-level course, you will get hands-on practice with machine learning at scale and how to employ the advanced ML infrastructure available on Google Cloud.
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Cloud Healthcare API bridges the gap between care systems and applications built on Google Cloud. By supporting standards-based data formats and protocols of existing healthcare technologies, Cloud Healthcare API connects your data to advanced Google Cloud capabilities, including streaming data processing with Cloud Dataflow, scalable analytics with BigQuery, and machine learning with Cloud Machine Learning Engine. In this Quest you will use the Cloud Healthcare API to ingest and process data in the industry standard FHIR, HL7v2 and DICOM formats, train a TensorFlow model for prediction with FHIR data, and also gain practice with de-identification of datasets.
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Looking to build or optimize your data warehouse? Learn best practices to Extract, Transform, and Load your data into Google Cloud with BigQuery. In this series of interactive labs you will create and optimize your own data warehouse using a variety of large-scale BigQuery public datasets. BigQuery is Google's fully managed, NoOps, low cost analytics database. With BigQuery you can query terabytes and terabytes of data without having any infrastructure to manage or needing a database administrator. BigQuery uses SQL and can take advantage of the pay-as-you-go model. BigQuery allows you to focus on analyzing data to find meaningful insights. Looking for a hands on challenge lab to demonstrate your skills and validate your knowledge? On completing this quest, enroll in and finish the additional challenge lab at the end of this quest to receive an exclusive Google Cloud digital badge.
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Data Catalog is deprecated and will be discontinued on January 30, 2026. You can still complete this course if you want to. For steps to transition your Data Catalog users, workloads, and content to Dataplex Catalog, see Transition from Data Catalog to Dataplex Catalog (https://cloud.google.com/dataplex/docs/transition-to-dataplex-catalog). Data Catalog is a fully managed and scalable metadata management service that empowers organizations to quickly discover, understand, and manage all of their data. In this quest you will start small by learning how to search and tag data assets and metadata with Data Catalog. After learning how to build your own tag templates that map to BigQuery table data, you will learn how to build MySQL, PostgreSQL, and SQLServer to Data Catalog Connectors.
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Complete the intermediate Engineer Data for Predictive Modeling with BigQuery ML skill badge to demonstrate skills in the following: building data transformation pipelines to BigQuery using Dataprep by Trifacta; using Cloud Storage, Dataflow, and BigQuery to build extract, transform, and load (ETL) workflows; and building machine learning models using BigQuery ML.
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This introductory-level quest shows application developers how the Google Cloud ecosystem could help them build secure, scalable, and intelligent cloud native applications. You learn how to develop and scale applications without setting up infrastructure, run data analytics, gain insights from data, and develop with pre-trained ML APIs to leverage machine learning even if you are not a Machine Learning expert. You will also experience seamless integration between various Google services and APIs to create intelligent apps.
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Ukończ szkolenie wprowadzające Uzyskiwanie statystyk z danych BigQuery, aby zdobyć odznakę potwierdzającą zdobycie następujących umiejętności: pisanie zapytań SQL, tworzenie zapytań dotyczących tabel publicznych, wczytywanie przykładowych danych w BigQuery, naprawianie typowych błędów składniowych przy użyciu walidatora zapytań w BigQuery oraz tworzenie raportów w Data Studio przez tworzenie połączenia z danymi BigQuery.
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Ukończ szkolenie wprowadzające Przygotowywanie danych do użycia z interfejsami ML w Google Cloud, aby zdobyć odznakę potwierdzającą zdobycie następujących umiejętności: czyszczenie danych przy użyciu usługi Dataprep firmy Trifacta, uruchamianie potoków danych w Dataflow, tworzenie klastrów i uruchamianie zadań Apache Spark w Managed Service for Apache Spark, a także wywoływanie interfejsów API dotyczących uczenia maszynowego, w tym Cloud Natural Language API, Google Cloud Speech-to-Text API oraz Video Intelligence API.
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Want to build ML models in minutes instead of hours using just SQL? BigQuery ML democratizes machine learning by letting data analysts create, train, evaluate, and predict with machine learning models using existing SQL tools and skills. In this series of labs, you will experiment with different model types and learn what makes a good model.
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Earn the advanced skill badge by completing the Use Machine Learning APIs on Google Cloud course, where you learn the basic features for the following machine learning and AI technologies: Cloud Vision API, Cloud Translation API, and Cloud Natural Language API.
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In this advanced-level quest, you will learn how to harness serious Google Cloud computing power to run big data and machine learning jobs. The hands-on labs will give you use cases, and you will be tasked with implementing big data and machine learning practices utilized by Google’s very own Solutions Architecture team. From running Big Query analytics on tens of thousands of basketball games, to training TensorFlow image classifiers, you will quickly see why Google Cloud is the go-to platform for running big data and machine learning jobs.
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TensorFlow is an open source software library for high performance numerical computation that's great for writing models that can train and run on platforms ranging from your laptop to a fleet of servers in the Cloud to an edge device. This quest takes you beyond the basics of using predefined models and teaches you how to build, train and deploy your own on Google Cloud.
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Google Cloud’s four step structured Cloud Migration Path Methodology provides a defined and repeatable path for users to follow when migrating and modernizing Virtual Machines. In this quest, you will get hands-on practice with Google’s current solution set for VM assessment, planning, migration, and modernization. You will start by analyzing your lab environment and building assessment reports with CloudPhysics and StratoZone, then build a landing zone within Google Cloud leveraging Terraform’s infrastructure-as-code templates, next you will manually transform a two-tier application into a cloud-native workload running on Kubernetes, and finally, transform a VM workload into Kubernetes with Migrate for Anthos and migrate a VM between cloud environments.
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Want to turn your marketing data into insights and build dashboards? Bring all of your data into one place for large-scale analysis and model building. Get repeatable, scalable, and valuable insights into your data by learning how to query it and using BigQuery. BigQuery is Google's fully managed, NoOps, low cost analytics database. With BigQuery you can query terabytes and terabytes of data without having any infrastructure to manage or needing a database administrator. BigQuery uses SQL and can take advantage of the pay-as-you-go model. BigQuery allows you to focus on analyzing data to find meaningful insights.
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Hellp Gamers! Up for a Qwik Challenge? Here's a fun game for you to test your GCP Skills. Challenge yourself to complete each task as quickly and accurately as possible to score points and earn badges! Take labs to earn points. The faster you complete the lab objectives, the higher your score.
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This advanced-level quest is unique amongst the other catalog offerings. The labs have been curated to give IT professionals hands-on practice with topics and services that appear in the Google Cloud Certified Professional Data Engineer Certification. From Big Query, to Dataprep, to Cloud Composer, this quest is composed of specific labs that will put your Google Cloud data engineering knowledge to the test. Be aware that while practice with these labs will increase your skills and abilities, you will need other preparation, too. The exam is quite challenging and external studying, experience, and/or background in cloud data engineering is recommended. Looking for a hands on challenge lab to demonstrate your skills and validate your knowledge? On completing this quest, enroll in and finish the additional challenge lab at the end of the Engineer Data in the Google Cloud to receive an exclusive Google Cloud digital badge.
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Want to scale your data analysis efforts without managing database hardware? Learn the best practices for querying and getting insights from your data warehouse with this interactive series of BigQuery labs. BigQuery is Google's fully managed, NoOps, low cost analytics database. With BigQuery you can query terabytes and terabytes of data without having any infrastructure to manage or needing a database administrator. BigQuery uses SQL and can take advantage of the pay-as-you-go model. BigQuery allows you to focus on analyzing data to find meaningful insights.
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Twelve years ago Lily started the Pet Theory chain of veterinary clinics, and has been expanding rapidly. Now, Pet Theory is experiencing some growing pains: their appointment scheduling system is not able to handle the increased load, customers aren't receiving lab results reliably through email and text, and veteranerians are spending more time with insurance companies than with their patients. Lily wants to build a cloud-based system that scales better than the legacy solution and doesn't require lots of ongoing maintenance. The team has decided to go with serverless technology. For the labs in the Google Cloud Run Serverless Quest, you will read through a fictitious business scenario in each lab and assist the characters in implementing a serverless solution. Looking for a hands on challenge lab to demonstrate your skills and validate your knowledge? On completing this quest, enroll in and finish the additional challenge lab at the end of this quest to receive an exclusive Google…
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The hands-on labs in this Quest are structured to give experienced app developers hands-on practice with the state-of-the-art developing applications in Google Cloud. The topics align with the Google Cloud Certified Professional Cloud Developer Certification. These labs follow the sequence of activities needed to create and deploy an app in Google Cloud from beginning to end. Be aware that while practice with these labs will increase your skills and abilities, it is recommended that you also review the exam guide and other available preparation resources.
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In this advanced-level quest, you will learn the ins and outs of developing GCP applications in Java. The first labs will walk you through the basics of environment setup and application data storage with Cloud Datastore. Once you have a handle on the fundamentals, you will get hands-on practice deploying Java applications on Kubernetes and App Engine (the latter is the same framework that powers Snapchat!) With specialized bonus labs that teach user authentication and backend service development, this quest will give you practical experience so you can start developing robust Java applications straight away.
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In this series of labs you will learn how to use BigQuery to analyze NCAA basketball data with SQL. Build a Machine Learning Model to predict the outcomes of NCAA March Madness basketball tournament games.
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Containerized applications have changed the game and are here to stay. With Kubernetes, you can orchestrate containers with ease, and integration with the Google Cloud Platform is seamless. In this advanced-level quest, you will be exposed to a wide range of Kubernetes use cases and will get hands-on practice architecting solutions over the course of 8 labs. From building Slackbots with NodeJS, to deploying game servers on clusters, to running the Cloud Vision API, Kubernetes Solutions will show you first-hand how agile and powerful this container orchestration system is.
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In this introductory-level quest, you will learn the fundamentals of developing and deploying applications on the Google Cloud Platform. You will get hands-on experience with the Google App Engine framework by launching applications written in languages like Python, Ruby, and Java (just to name a few). You will see first-hand how straightforward and powerful GCP application frameworks are, and how easily they integrate with GCP database, data-loss prevention, and security services.
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This quest is designed to teach you how to apply AWS Identity and Access Management, in concert with several other AWS Services, to address real-world application and service security management scenarios.
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Obtain a competitive advantage through DevOps. DevOps is an organizational and cultural movement that aims to increase software delivery velocity, improve service reliability, and build shared ownership among software stakeholders. In this course you will learn how to use Google Cloud to improve the speed, stability, availability, and security of your software delivery capability. DevOps Research and Assessment has joined Google Cloud. How does your team measure up? Take this five question multiple-choice quiz and find out!
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Kubernetes is the most popular container orchestration system, and Google Kubernetes Engine was designed specifically to support managed Kubernetes deployments in Google Cloud. In this course, you will get hands-on practice configuring Docker images, containers, and deploying fully-fledged Kubernetes Engine applications.
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This quest of "Challenge Labs" gives the student preparing for the Google Cloud Certified Professional Cloud Architect certification hands-on practice with common business/technology solutions using Google Cloud architectures. Challenge Labs do not provide the "cookbook" steps, but require solutions to be built with minimal guidance, across many Google Cloud technologies. All labs have activity tracking, and in order to earn this badge you must score 100% in each lab. This quest is not easy and will put your Google Cloud technology skills to the test! Be aware that while practice with these labs will increase your knowledge and abilities, additional study, experience, and background in cloud architecture is recommended to prepare for this certification. Complete this quest to receive an exclusive Google Cloud digital badge.
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Ten kurs wprowadzający jest inny niż wszystkie pozostałe oferowane kursy. Jego moduły zostały wybrane tak, aby udostępnić specjalistom z branży IT praktyczne ćwiczenia w zakresie tematów i usług, znajomość których jest potrzebna do uzyskania certyfikatu Google Cloud Associate Cloud Engineer. Od uprawnień i zarządzania siecią po wdrażanie Kubernetes Engine – ten kurs składa się ze specjalnych modułów, w trakcie których sprawdzisz swoją wiedzę na temat Google Cloud. Ćwiczenia w ramach tych modułów pozwolą Ci zwiększyć swoje umiejętności i kompetencje, ale zalecamy również zapoznanie się z przewodnikiem po egzaminie i innymi dostępnymi materiałami przygotowawczymi.
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This fundamental-level quest is unique amongst the other quest offerings. The labs have been curated to give IT professionals hands-on practice with topics and services that appear in the Google Cloud Certified Professional Cloud Architect Certification. From IAM, to networking, to Kubernetes engine deployment, this quest is composed of specific labs that will put your Google Cloud knowledge to the test. Be aware that while practice with these labs will increase your skills and abilities, we recommend that you also review the exam guide and other available preparation resources.
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Jeśli dopiero zaczynasz programować w chmurze i szukasz praktycznych ćwiczeń wykraczających poza treści z kursu „Podstawy Google Cloud”, ten kurs jest dla Ciebie. Zdobędziesz praktyczne doświadczenie dzięki modułom poświęconym Cloud Storage i innym kluczowym usługom aplikacji, takim jak Monitoring i Cloud Functions. Zdobędziesz cenne umiejętności, które przydadzą się w każdym przedsięwzięciu z zastosowaniem Google Cloud.
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Big data, uczenie maszynowe i sztuczna inteligencja to najpopularniejsze tematy współczesnej informatyki, jednak to dość wyspecjalizowane dziedziny i trudno znaleźć materiały wprowadzające do nich. Na szczęście Google Cloud udostępnia przyjazne dla użytkownika usługi w tych obszarach, a dzięki temu kursowi dla początkujących możesz poznać podstawy narzędzi takich jak BigQuery, Cloud Speech API i Video Intelligence.
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It’s no secret that machine learning is one of the fastest growing fields in tech, and the Google Cloud Platform has been instrumental in furthering its development. With a host of APIs, GCP has a tool for just about any machine learning job. In this introductory course, you will get hands-on practice with machine learning as it applies to language processing by taking labs that will enable you to extract entities from text, and perform sentiment and syntactic analysis as well as use the Speech to Text API for transcription.
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Na tym kursie dla początkujących nauczysz się używać podstawowych narzędzi i usług Google Cloud. Dostępne są opcjonalne filmy zapewniające szerszy kontekst i pomagające powtórzyć pojęcia omówione podczas laboratoriów. Podstawy Google Cloud to kurs zalecany jako pierwszy dla osób poznających Google Cloud – możesz zacząć naukę, mając minimalną albo nawet zerową wiedzę o chmurze, a skończyć z praktycznymi umiejętnościami, które wykorzystasz w swoim pierwszym projekcie Google Cloud. Od wpisywania poleceń w Cloud Shell i wdrażania pierwszej maszyny wirtualnej po uruchamianie aplikacji w Kubernetes Engine lub aplikacji z równoważeniem obciążenia, Podstawy Google Cloud to doskonałe wprowadzenie do podstawowych funkcji platformy.
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It's no secret that machine learning is one of the fastest growing fields in tech, and Google Cloud has been instrumental in furthering its development. With a host of APIs, Google Cloud has a tool for just about any machine learning job. In this advanced-level course, you will get hands-on practice with machine learning APIs by taking labs like Detect Labels, Faces, and Landmarks in Images with the Cloud Vision API. Looking for a hands-on challenge lab to demonstrate your skills and validate your knowledge? Enroll in and finish the additional challenge lab at the end of this quest to receive an exclusive Google Cloud digital badge.
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Using large scale computing power to recognize patterns and "read" images is one of the foundational technologies in AI, from self-driving cars to facial recognition. The Google Cloud Platform provides world class speed and accuracy via systems that can utilized by simply calling APIs. With these and a host of other APIs, GCP has a tool for just about any machine learning job. In this introductory quest, you will get hands-on practice with machine learning as it applies to image processing by taking labs that will enable you to label images, detect faces and landmarks, as well as extract, analyze, and translate text from within images.
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Learn how the Google Cloud ecosystem can help you build secure, scalable and intelligent cloud native applications and experience seamless integration between Google services and APIs to create intelligent apps. Dive deep into key solutions to common use cases and gain hands-on experience with App Engine, Stackdriver and the Places API, plus score points and compete with other players in today's game.
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Welcome, gamers! Cloud Hero is played around the world, in person and online. Today, you have the opportunity to become one of the Cloud Heroes! Today's game is all about Big Data and Machine Learning. You will compete to see who can finish the game with the most points. Speed is important, and so is accuracy! Start taking the game labs to score points. Earn the most points by completing the steps in the lab.... and get bonus points for speed! Be sure to click "End" when you're done with the lab to get the maximum points. You can take each lab up to 5 times.
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Cloud Hero is played around the world, in person and online. Today, you have the opportunity to become one of the Cloud Heroes! Today's game is all about Core Infrastructure. You will compete to see who can finish the game with the most points. Speed is important, and so is accuracy! Earn the most points by completing the steps in the lab.... and get bonus points for speed! Be sure to click "End" when you're done with each lab to get the maximum points. All players will be awarded the game badge.
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