Einstieg in die Bildgenerierung
Earned Jul 4, 2023 EDT
Verantwortungsbewusste Anwendung von KI: KI-Grundsätze in Google Cloud anwenden
Earned Jul 4, 2023 EDT
Generative AI Fundamentals
Earned Jul 4, 2023 EDT
Einführung in die verantwortungsbewusste Anwendung von KI
Earned Jul 4, 2023 EDT
Einführung in Large Language Models
Earned Jul 4, 2023 EDT
Einführung in generative KI
Earned Jul 4, 2023 EDT
Serverless Data Processing with Dataflow: Foundations
Earned Jul 4, 2023 EDT
Smart Analytics, Machine Learning, and AI on Google Cloud
Earned Jul 4, 2023 EDT
Building Resilient Streaming Systems on Google Cloud Platform
Earned Jul 4, 2023 EDT
Build Batch Data Pipelines on Google Cloud
Earned Jul 3, 2023 EDT
Build Data Lakes and Data Warehouses on Google Cloud
Earned Jul 3, 2023 EDT
Preparing for your Professional Data Engineer Journey
Earned Jul 3, 2023 EDT
Monitoring und Logging mit Google Cloud Observability
Earned Jul 3, 2023 EDT
DevOps-Workflows in Google Cloud implementieren
Earned Jul 2, 2023 EDT
DEPRECATED Cloud Operations and Service Mesh with Anthos
Earned Jul 2, 2023 EDT
Building No-Code Apps with AppSheet: Foundations
Earned Jun 30, 2023 EDT
[DEPRECATED] Google Cloud Big Data and Machine Learning Fundamentals
Earned Jun 30, 2023 EDT
Managing Security in Google Cloud
Earned Jun 28, 2023 EDT
Preparing for Your Professional Cloud Security Engineer Journey
Earned Jun 28, 2023 EDT
Daten für ML-APIs in Google Cloud vorbereiten
Earned Jun 27, 2023 EDT
Developing a Google SRE Culture
Earned Jun 27, 2023 EDT
Kubernetes-Anwendungen in Google Cloud bereitstellen
Earned Jun 27, 2023 EDT
Serverlose Apps mit Firebase entwickeln
Earned Jun 27, 2023 EDT
Serverlose Anwendungen in Cloud Run entwickeln
Earned Jun 26, 2023 EDT
Hybrid Cloud Modernizing Applications with Anthos
Earned Jun 26, 2023 EDT
Application Development with Cloud Run
Earned Jun 26, 2023 EDT
App Deployment, Debugging, and Performance
Earned Jun 26, 2023 EDT
Securing and Integrating Components of your Application
Earned Jun 26, 2023 EDT
Getting Started With Application Development
Earned Jun 25, 2023 EDT
Architecting with Google Kubernetes Engine: Workloads
Earned Jun 23, 2023 EDT
Architecting with Google Kubernetes Engine: Foundations
Earned Jun 23, 2023 EDT
Grundlegende Sicherheitsfunktionen in Google Cloud implementieren
Earned Jun 23, 2023 EDT
DEPRECATED Network Performance and Optimization
Earned Jun 22, 2023 EDT
Geschütztes Google Cloud-Netzwerk erstellen
Earned Jun 22, 2023 EDT
Networking in Google Cloud: Routing and Addressing
Earned Jun 22, 2023 EDT
Networking in Google Cloud: Fundamentals
Earned Jun 22, 2023 EDT
Automate Deployment and Manage Traffic on a Google Cloud Network
Earned Jun 22, 2023 EDT
Preparing for Your Professional Cloud Network Engineer Journey
Earned Jun 21, 2023 EDT
Cloud-Architektur: Entwerfen, umsetzen und verwalten
Earned Jun 21, 2023 EDT
Kostenoptimierung für die Google Kubernetes Engine
Earned Jun 20, 2023 EDT
Google Cloud-Netzwerk einrichten
Earned Jun 19, 2023 EDT
Reliable Google Cloud Infrastructure: Design and Process
Earned Jun 19, 2023 EDT
Build a Certification Study Guide: PCA Exam Prep
Earned Jun 19, 2023 EDT
Scaling with Google Cloud Operations
Earned Jun 18, 2023 EDT
Modernize Infrastructure and Applications with Google Cloud
Earned Jun 18, 2023 EDT
Exploring Data Transformation with Google Cloud
Earned Jun 18, 2023 EDT
Digital Transformation with Google Cloud
Earned Jun 18, 2023 EDT
Infrastruktur mit Terraform in Google Cloud erstellen
Earned Jun 17, 2023 EDT
Google Cloud-Netzwerk entwickeln
Earned Jun 17, 2023 EDT
Umgebung für die Anwendungsentwicklung in Google Cloud einrichten
Earned Jun 16, 2023 EDT
Cloud Load Balancing in der Compute Engine implementieren
Earned Jun 16, 2023 EDT
Getting Started with Terraform for Google Cloud
Earned Jun 16, 2023 EDT
Logging and Monitoring in Google Cloud
Earned Jun 16, 2023 EDT
Erste Schritte mit der Google Kubernetes Engine
Earned Jun 15, 2023 EDT
Elastische Google Cloud-Infrastruktur: Skalierung und Automatisierung
Earned Jun 15, 2023 EDT
Einführung in die Google Cloud-Infrastruktur: Hauptdienste
Earned Jun 14, 2023 EDT
Einführung in die Google Cloud-Infrastruktur: Grundlagen
Earned Jun 14, 2023 EDT
Google Cloud-Grundlagen: Kerninfrastruktur
Earned Jun 13, 2023 EDT
Vorbereitung auf die Prüfung zum Associate Cloud Engineer
Earned Jun 12, 2023 EDT
Google Sheets - Advanced Topics
Earned Jun 9, 2023 EDT
Google Meet
Earned Jun 9, 2023 EDT
Google Slides
Earned Jun 9, 2023 EDT
Google Sheets
Earned Jun 9, 2023 EDT
Google Docs
Earned Jun 9, 2023 EDT
Google Drive
Earned Jun 9, 2023 EDT
Google Calendar
Earned Jun 8, 2023 EDT
Gmail
Earned Jun 8, 2023 EDT
Configure your Workplace: Google Workspace for IT Admins
Earned Jun 8, 2023 EDT
Cloud-Zusammenarbeit und Produktivitäts-Workflows implementieren
Earned Jun 8, 2023 EDT
DEPRECATED Planning for a Google Workspace Deployment
Earned Jun 7, 2023 EDT
Data Governance bei der Google Workspace-Nutzung
Earned Jun 6, 2023 EDT
Sicherheit in Google Workspace
Earned Jun 6, 2023 EDT
Google Workspace-Hauptdienste
Earned Jun 6, 2023 EDT
Nutzer- und Ressourcenverwaltung in Google Workspace
Earned Jun 6, 2023 EDT
This workload aims to upskill Google Cloud partners to perform specific tasks for modernization using LookML on BigQuery. A proof-of-concept will take learners through the process of creating LookML visualizations on BigQuery. During this course, learners will be guided specifically on how to write Looker modeling language, also known as LookML and create semantic data models, and learn how LookML constructs SQL queries against BigQuery. At a high level, this course will focus on basic LookML to create and access BigQuery objects, and optimize BigQuery objects with LookML.
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Die Kursreihe „Einführung in das Cloud-Computing von Google“ richtet sich an Personen mit geringen oder gar keinen Vorkenntnissen oder Erfahrungen im Bereich Cloud Computing. Sie bietet einen detaillierten Überblick über Cloud-Grundlagen, Big Data, maschinelles Lernen und die Rolle von Google Cloud in diesem Bereich.
Am Ende der Kursreihe können Teilnehmende diese Konzepte erläutern und einige praktische Fähigkeiten demonstrieren.
Die Kurse sollten in folgender Reihenfolge absolviert werden:
1. Einführung in das Cloud-Computing von Google: Cloud-Computing-Grundlagen
2. Einführung in das Cloud-Computing von Google: Infrastruktur in Google Cloud
3. Einführung in das Cloud-Computing von Google: Netzwerke und Sicherheit in Google Cloud
4. Einführung in das Cloud-Computing von Google: Daten, ML und KI in Google Cloud
Diese Kursreihe bietet einen Überblick über Cloud-Computing, verschiedene Nutzungsmöglichkeiten von Google Cloud und verschiedene Computing-Optionen.
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Mit dem Skill-Logo zum Kurs Datenmodelle in Looker verwalten weisen Sie fortgeschrittene Kenntnisse in den folgenden Bereichen nach: Aufrechterhaltung des Zustands von LookML-Projekten, Verwendung von SQL-Runner zur Datenvalidierung, Umsetzung von Best Practices für LookML, Leistungsoptimierung von Abfragen und Berichten sowie Implementierung von persistenten abgeleiteten Tabellen und Caching-Richtlinien.
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Mit dem Skill-Logo zum Einsteigerkurs LookML-Objekte in Looker erstellen weisen Sie Kenntnisse über Folgendes nach: neue Dimensionen und Messwerte, Ansichten und abgeleitete Tabellen erstellen, Messwertfilter und Typen basierend auf Anforderungen festlegen, Dimensionen und Messwerte aktualisieren, Explores erstellen und verfeinern, Ansichten mit vorhandenen Explores verknüpfen und entscheiden, welche LookML- Objekte aufgrund von Geschäftsanforderungen erstellt werden sollen.
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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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Mit dem Skill-Logo zum Kurs ML-Modelle mit BigQuery ML erstellen weisen Sie fortgeschrittene Kenntnisse in folgendem Bereich nach: Erstellen und Bewerten von Machine-Learning-Modellen mit BigQuery ML, um Datenvorhersagen zu treffen.
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Mit dem Skill-Logo zum Kurs Informationen aus BigQuery-Daten ableiten weisen Sie Grundkenntnisse in folgenden Bereichen nach: Schreiben von SQL-Abfragen, Abfragen öffentlicher Tabellen, Laden von Beispieldaten in BigQuery, Beheben häufig auftretender Syntaxfehler mithilfe der Abfragevalidierung in BigQuery und Erstellen von Berichten in Data Studio durch Herstellen einer Verbindung zu BigQuery-Daten.
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In this course, we define what machine learning is and how it can benefit your business. You'll see a few demos of ML in action and learn key ML terms like instances, features, and labels. In the interactive labs, you will practice invoking the pretrained ML APIs available as well as build your own Machine Learning models using just SQL with BigQuery ML.
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The third course in this course series is Achieving Advanced Insights with BigQuery. Here we will build on your growing knowledge of SQL as we dive into advanced functions and how to break apart a complex query into manageable steps.
We will cover the internal architecture of BigQuery (column-based sharded storage) and advanced SQL topics like nested and repeated fields through the use of Arrays and Structs. Lastly we will dive into optimizing your queries for performance and how you can secure your data through authorized views.
After completing this course, enroll in the Applying Machine Learning to your Data with Google Cloud course.
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This is the second course in the Data to Insights course series. Here we will cover how to ingest new external datasets into BigQuery and visualize them with Looker Studio. We will also cover intermediate SQL concepts like multi-table JOINs and UNIONs which will allow you to analyze data across multiple data sources.
Note: Even if you have a background in SQL, there are BigQuery specifics (like handling query cache and table wildcards) that may be new to you.
After completing this course, enroll in the Achieving Advanced Insights with BigQuery course.
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In this course, we see what the common challenges faced by data analysts are and how to solve them with the big data tools on Google Cloud. You’ll pick up some SQL along the way and become very familiar with using BigQuery and Dataprep to analyze and transform your datasets.
This is the first course of the From Data to Insights with Google Cloud series. After completing this course, enroll in the Creating New BigQuery Datasets and Visualizing Insights course.
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This course enables system integrators and partners to understand the principles of automated migrations, plan legacy system migrations to Google Cloud leveraging G4 Platform, and execute a trial code conversion.
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Migration from MySQL to Cloud SQL using Database Migration Service that includes sample mock data and all necessary steps with initial assessment to validation including taking care of migrating users and grants.
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Migration from MySQL to Cloud Spanner using Dataflow that includes sample mock data and all necessary steps with initial assessment to validation including taking care of migrating users and grants.
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This course enables partners to help customers extend their on-premises VMware environments to Google Cloud using Google Cloud VMware Engine. Partners will learn how to architect Google Cloud VMware Engine migration solutions that allow customers to move virtual machines to the cloud with minimal risk. The course also instructs partners on how to solve real-world VMware integration and migration problems.
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(Previously named "Developing apps with Vertex AI Agent Builder: Search". Please note there maybe instances in this course where previous product names and titles are used)
Enterprises of all sizes have trouble making their information readily accessible to employees and customers alike. Internal documentation is frequently scattered across wikis, file shares, and databases. Similarly, consumer-facing sites often offer a vast selection of products, services, and information, but customers are frustrated by ineffective site search and navigation capabilities.
This course teaches you to use AI Applications to integrate enterprise-grade generative AI search.
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This course empowers you to develop scalable, performant LookML (Looker Modeling Language) models that provide your business users with the standardized, ready-to-use data that they need to answer their questions. Upon completing this course, you will be able to start building and maintaining LookML models to curate and manage data in your organization’s Looker instance.
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In this course, you learn how to do the kind of data exploration and analysis in Looker that would formerly be done primarily by SQL developers or analysts. Upon completion of this course, you will be able to leverage Looker's modern analytics platform to find and explore relevant content in your organization’s Looker instance, ask questions of your data, create new metrics as needed, and build and share visualizations and dashboards to facilitate data-driven decision making.
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In diesem Kurs sammeln Sie praktische Erfahrungen mit dem Anwenden erweiterter LookML-Konzepte in Looker. Sie lernen, wie Sie Liquid verwenden, um dynamische Dimensionen und Measures zu erstellen und anzupassen. Außerdem erfahren Sie, wie Sie dynamische abgeleitete SQL-Tabellen und benutzerdefinierte native abgeleitete Tabellen erstellen sowie Erweiterungen zur Modularisierung Ihres LookML-Codes nutzen.
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In this quest, 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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MMit dem Skill-Logo zum Kurs Daten für Looker-Dashboards und ‑Berichte vorbereiten weisen Sie Grundkenntnisse in folgenden Bereichen nach: Filtern, Sortieren und Pivotieren von Daten, Zusammenführen der Ergebnisse von verschiedenen Looker-Explores sowie Verwenden von Funktionen und Operatoren zum Erstellen von Looker-Dashboards und ‑Berichten für Analyse und Visualisierung von Daten.
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Earn a skill badge by passing the final quiz, you'll demonstrate your understanding of foundational concepts in generative AI.
A skill badge is a digital badge issued by Google Cloud in recognition of your knowledge of Google Cloud products and services. Share your skill badge by making your profile public and adding it to your social media profile.
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This course explores the Geographic Information Systems (GIS), GIS Visualization, and machine learning enhancements to BigQuery.
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This course explores how to implement a streaming analytics solution using Dataflow and BigQuery.
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This course explores how to leverage Looker to create data experiences and gain insights with modern business intelligence (BI) and reporting.
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This course explores how to implement a streaming analytics solution using Pub/Sub.
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This course continues to explore the implementation of data load and transformation pipelines for a BigQuery Data Warehouse using Dataflow.
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This course continues to explore the implementation of data load and transformation pipelines for a BigQuery Data Warehouse using Cloud Data Fusion.
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This course explores the implementation of data load and transformation pipelines for a BigQuery Data Warehouse using Dataproc.
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This course identifies best practices for migrating data warehouses to BigQuery and the key skills required to perform successful migration.
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Verschaffen Sie sich einen Wettbewerbsvorteil durch DevOps. DevOps ist eine organisationsbezogene und kulturelle Bewegung, deren Ziel darin besteht, Software schneller bereitzustellen, die Zuverlässigkeit der Dienste zu verbessern und ein gemeinsames Verantwortungsbewusstsein unter den Software-Stakeholdern aufzubauen. In diesem Kurs erfahren Sie, wie Sie mit Google Cloud die Geschwindigkeit, Stabilität, Verfügbarkeit und Sicherheit bei der Softwarebereitstellung verbessern. DevOps Research and Assessment ist jetzt Teil von Google Cloud. Wie sieht es bei Ihrem Team aus? Absolvieren Sie dieses Multiple-Choice-Quiz mit fünf Fragen und finden Sie es heraus!
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Sichern Sie sich das Skill-Logo für Fortgeschrittene, indem Sie den Kurs CI/CD-Pipelines in Google Cloud umsetzen abschließen. In diesem Kurs lernen Sie, wie Sie Artifact Registry, Cloud Build und Cloud Deploy verwenden. Sie interagieren mit der Google Cloud Console, Google Cloud CLI, GKE und mit Cloud Run. Sie erfahren, wie Sie Pipelines für die Continuous Integration entwickeln, Artefakte speichern und sichern, Scannen auf Sicherheitslücken durchführen und die Gültigkeit genehmigter Releases bestätigen. Außerdem erhalten Sie praxisorientierte Einblicke in die Bereitstellung von Anwendungen in der GKE und in Cloud Run.
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Sicherheit hat bei Google Cloud-Diensten oberste Priorität. Google Cloud verfügt daher über spezielle Tools, die beim Thema Schutz und Identität für Sicherheit in Ihren Projekten sorgen. In diesem Kurs für Einsteiger sammeln Sie praktische Erfahrungen mit Google Cloud Identity and Access Management (IAM), einer erprobten Lösung für die Verwaltung von Nutzer- und VM-Konten. Außerdem erhalten Sie Einblicke in die Netzwerksicherheit, indem Sie Virtual Private Clouds (VPCs) und virtuelle private Netzwerke (VPNs) bereitstellen. Darüber hinaus lernen Sie, welche Tools Sie zum Schutz vor Sicherheitsbedrohungen und Datenverlusten einsetzen können.
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This course, Architecting with Google Kubernetes Engine: Workloads - Locales, is intended for non-English learners. If you want to take this course in English, please enroll in Architecting with Google Kubernetes Engine: Workloads.
In this course, "Architecting with Google Kubernetes Engine: Workloads," you learn about performing Kubernetes operations; creating and managing deployments; the tools of GKE networking; and how to give your Kubernetes workloads persistent storage.
This is the second course of the Architecting with Google Kubernetes Engine series. After completing this course, enroll in the Reliable Google Cloud Infrastructure: Design and Process course or the Hybrid Cloud Infrastructure Foundations with Anthos course.
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"This course, Architecting with Google Kubernetes Engine: Foundations - Locales, is intended for non-English learners. If you want to take this course in English, please enroll in Architecting with Google Kubernetes Engine: Foundations". In this course, "Architecting with Google Kubernetes Engine: Foundations," you get a review of the layout and principles of Google Cloud, followed by an introduction to creating and managing software containers and an introduction to the architecture of Kubernetes.
This is the first course of the Architecting with Google Kubernetes Engine series. After completing this course, enroll in the Architecting with Google Kubernetes Engine: Workloads course.
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This course is the third part of the SAP on Google Cloud Platform learning path. Following the SAP on Google Cloud Foundations eLearning and the SAP on Google Cloud Self-paced labs. Participants should have completed these two components before. This course consists of hands-on labs that provide a holistic experience of optimally configuring SAP on Google Cloud. Participants will learn to configure SAP on Google Cloud, and what best practices are, leaving the course with actionable experience to configure SAP on Google Cloud and run SAP workloads on Google Cloud for their customers.
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This on-demand course equips students to understand, configure, and maintain
multi-cluster Kubernetes infrastructures using Anthos GKE, and Istio-based
service mesh, whether deployed with Anthos on Google Cloud or with Anthos
deployed on VMware.
This is the third, and final, course of the Architecting Hybrid Cloud
Infrastructure with Anthos series.
Completion of the Architecting with Google Kubernetes Engine path is a
prerequisite for this course.
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Welcome to Hybrid Cloud Infrastructure Foundations with Anthos!
This is the first course of the Architecting Hybrid Cloud Infrastructure with Anthos path. Anthos enables you to build and manage modern applications, and gives you the freedom to choose where to run them. Anthos gives you one consistent experience in both your on-premises and cloud environments.
During this course, you will be presented with modules that will take you through skills that you will use as an architect or administrator running Anthos environments. The modules in this course include videos, hands-on labs, and links to helpful documentation.
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In this course, you'll learn about Kubernetes and Google Kubernetes Engine (GKE) security; logging and monitoring; and using Google Cloud managed storage and database services from within GKE.
This is the second course of the Architecting with Google Kubernetes Engine series. After completing this course, enroll in the Reliable Google Cloud Infrastructure: Design and Process course or the Hybrid Cloud Infrastructure Foundations with Anthos course.
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Erhalten Sie ein Skill-Logo für den Abschluss des Kurses Apigee X bereitstellen und verwalten. Der Kurs behandelt folgende Themen: Architektur von Apigee X, Bereitstellung einer Apigee X-Organisation innerhalb eines Google Cloud-Projekts, Verwaltung von Apigee X mit der Apigee-API und ‑Benutzeroberfläche sowie Richtlinien zum Bedrohungsschutz von Cloud Armor und Apigee zum Schutz Ihrer APIs.
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Learn how to upgrade capacity for the Apigee for private cloud platform installation, and how to monitor the platform. This is the third and final course of the Installing and Managing Google Cloud's Apigee API Platform for Private Cloud series.
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This course discusses the management and operation of the Apigee platform for private cloud.
It includes topics on operational practices, API deployment, analytics, security and upgrade of the platform. This is the second course of the Installing and Managing Google Cloud's Apigee API Platform for Private Cloud course series. After completing this course, enroll in the On Premises Capacity Upgrade and Monitoring with Google Cloud's Apigee API Platform course.
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This course discusses the upgrade process for Apigee hybrid, and teaches you how to monitor and troubleshoot the hybrid runtime plane components.
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This course discusses how environments are managed in Apigee hybrid, and how runtime plane components are secured. You will also learn how to deploy and debug API proxies in Apigee hybrid, and about capacity planning and scaling.
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This course introduces you to the fundamentals and practices used to install and manage Google Cloud's Apigee API Platform for hybrid cloud.
Through a combination of lectures, a hands-on lab, and supplemental materials, you will learn how to install and operate the Apigee API Platform.
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In this course, you learn how to create APIs that utilize multiple services and how you can use custom code on Apigee. You will also learn about fault handling, and how to share logic between proxies. You learn about traffic management and caching. You also create a developer portal, and publish your API to the portal. You learn about logging and analytics, as well as CI/CD and the different deployment models supported by Apigee.
Through a combination of lectures, hands-on labs, and supplemental materials, you will learn how to design, build, secure, deploy, and manage API solutions using Google Cloud's Apigee API Platform.
This is the third and final course of the Developing APIs with the Apigee API Platform course series.
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In this course, you learn how to secure your APIs. You explore the security concerns you will encounter for your APIs. You learn about OAuth, the primary authorization method for REST APIs. You will learn about JSON Web Tokens (JWTs) and federated security. You also learn about securing against malicious requests, safely sending requests across a public network, and how to secure your data for users of Apigee.
Through a combination of lectures, hands-on labs, and supplemental materials, you will learn how to design, build, secure, deploy, and manage API solutions using Google Cloud's Apigee API Platform.
This is the second course of the Developing APIs with the Apigee API Platform series. After completing this course, enroll in the API Development with the Apigee API Platform course.
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In this course, you learn how to design APIs, and how to use OpenAPI specifications to document them. You learn about the API lifecycle, and how the Apigee API platform helps you manage all aspects of the lifecycle. You learn how APIs can be designed using API proxies, and how APIs are packaged as API products to be used by app developers.
Through a combination of lessons, hands-on labs, and supplemental materials, you will learn how to design, build, secure, deploy, and manage API solutions using Google Cloud's Apigee API Platform. This is the first course of the Developing APIs with the Apigee API Platform series. After completing this course, enroll in the API Security with the Apigee API Platform course.
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The Cloud Foundations Customer Onboarding: Best Practices course enables partners to onboard customers on Google Cloud efficiently and in minimum time, by imparting knowledge, IP, and best practices from the Technical Onboarding Center (TOC) team at Global Delivery Center (GDC). The course explores Cloud Identity and organization, users and groups, administrative access, and resource hierarchy. It also examines network configuration, hybrid connectivity, logging and monitoring, and organizational security.
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This course introduces participants to MLOps tools and best practices for deploying, evaluating, monitoring and operating production ML systems on Google Cloud. MLOps is a discipline focused on the deployment, testing, monitoring, and automation of ML systems in production. Machine Learning Engineering professionals use tools for continuous improvement and evaluation of deployed models. They work with (or can be) Data Scientists, who develop models, to enable velocity and rigor in deploying the best performing models.
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In this course, you apply your knowledge of classification models and embeddings to build a ML pipeline that functions as a recommendation engine.
This is the fifth and final course of the Advanced Machine Learning on Google Cloud series.
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This course introduces the products and solutions to solve NLP problems on Google Cloud. Additionally, it explores the processes, techniques, and tools to develop an NLP project with neural networks by using Vertex AI and TensorFlow.
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This course describes different types of computer vision use cases and then highlights different machine learning strategies for solving these use cases. The strategies vary from experimenting with pre-built ML models through pre-built ML APIs and AutoML Vision to building custom image classifiers using linear models, deep neural network (DNN) models or convolutional neural network (CNN) models.
The course shows how to improve a model's accuracy with augmentation, feature extraction, and fine-tuning hyperparameters while trying to avoid overfitting the data.
The course also looks at practical issues that arise, for example, when one doesn't have enough data and how to incorporate the latest research findings into different models.
Learners will get hands-on practice building and optimizing their own image classification models on a variety of public datasets in the labs they will work on.
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This course covers BigQuery fundamentals for professionals who are familiar with SQL-based cloud data warehouses in Redshift and want to begin working in BigQuery. Through interactive lecture content and hands-on labs, you learn how to provision resources, create and share data assets, ingest data, and optimize query performance in BigQuery. Drawing upon your knowledge of Redshift, you also learn about similarities and differences between Redshift and BigQuery to help you get started with data warehouses in BigQuery. After this course, you can continue your BigQuery journey by completing the skill badge quest titled Build and Optimize Data Warehouses with BigQuery.
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This course aims to upskill Google Cloud partners to perform specific tasks of migrating data from Microsoft SQL Server to CloudSQL using the built-in replication capabilities of SQL Server.
Sample data will be used during the migration. Learners will complete several labs that focus on the process of transferring schema, data, and related processes to corresponding Google Cloud products.
One or more challenge labs will test the learner's understanding of the topics.
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This workload aims to upskill Google Cloud partners to perform specific tasks associated with priority workloads. Learners will perform the tasks of migrating data from five products hosted on Cloudera or Hortonworks to corresponding Google Cloud services and hosted products. The migration solutions addressed will be:
HDFS data to Google Cloud Dataproc and Cloud Storage
Hive data to Cloud Dataproc and the Cloud Dataproc Metastore
Hive data to Google Cloud BigQuery
Impala data to Google Cloud BigQuery
HBase to Google Cloud Bigtable
Sample data will be used during all five migrations. Learners will complete several labs that focus on the process of transferring schema, data and related processes to corresponding Google Cloud products.There will be one or more challenge labs that will test the learners understanding of the topics.
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Welcome to Optimize in BigQuery, where we map Enterprise Data Warehouse concepts and components to BigQuery and Google data services with a focus on optimization.
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Welcome to Design in BigQuery, where we map Enterprise Data Warehouse concepts and components to BigQuery and Google data services with a focus on schema design.
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This course discusses the key elements of Google's Data Warehouse solution portfolio and strategy.
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This is the fourth course of a four-course series for cloud architects and engineers with existing Azure knowledge. It compares Google Cloud and Azure solutions and guides professionals on their use.
This course focuses on deploying and monitoring applications in Google Cloud. The learners apply the knowledge of monitoring and application deployment process in Azure to explore the differences with Google Cloud. Learners get hands-on practice building and managing Google Cloud resources.
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This is the third course of a four-course series for cloud architects and engineers with existing Azure knowledge. It compares Google Cloud and Azure solutions and guides professionals on their use.
This course focuses on storage options and containers in Google Cloud. The learners apply the knowledge of storage and containers in Azure to explore how they differ from Google Cloud. Learners get hands-on practice building and managing Google Cloud resources.
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This is the second course of a four-course series for cloud architects and engineers with existing Azure knowledge. It aims to compare Google Cloud and Azure solutions and guide professionals on their use.
This course focuses on compute resources and load balancing in Google Cloud. The learner will apply the knowledge of using virtual machines and load balancers in Azure to explore the similarities and differences with configuring and managing compute resources and load balancers in Google Cloud. Learners will get hands-on practice building and managing Google Cloud resources.
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This is the first course of a four-course series for cloud architects and engineers with existing Azure knowledge, and it compares Google Cloud and Azure solutions and guides professionals on their use.
This course focuses on Identity and Access Management (IAM) and networking in Google Cloud. The learners apply the knowledge of access management and networking in Azure to explore the similarities and differences with access management and networking in Google Cloud. Learners get hands-on practice building and managing Google Cloud resources.
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Get Anthos Ready. This Google Kubernetes Engine-centric quest of best practice hands-on labs focuses on security at scale when deploying and managing production GKE environments -- specifically role-based access control, hardening, VPC networking, and binary authorization.
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In this self-paced training course, participants learn mitigations for attacks at many points in a Google Cloud-based infrastructure, including Distributed Denial-of-Service attacks, phishing attacks, and threats involving content classification and use. They also learn about the Security Command Center, cloud logging and audit logging, and using Forseti to view overall compliance with your organization's security policies.
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This self-paced training course gives participants broad study of security controls and techniques on Google Cloud. Through recorded lectures, demonstrations, and hands-on labs, participants explore and deploy the components of a secure Google Cloud solution, including Cloud Storage access control technologies, Security Keys, Customer-Supplied Encryption Keys, API access controls, scoping, shielded VMs, encryption, and signed URLs. It also covers securing Kubernetes environments.
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This course covers how to implement the various flavors of production ML systems— static, dynamic, and continuous training; static and dynamic inference; and batch and online processing. You delve into TensorFlow abstraction levels, the various options for doing distributed training, and how to write distributed training models with custom estimators.
This is the second course of the Advanced Machine Learning on Google Cloud series. After completing this course, enroll in the Image Understanding with TensorFlow on Google Cloud course.
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This course takes a real-world approach to the ML Workflow through a case study. An ML team faces several ML business requirements and use cases. The team must understand the tools required for data management and governance and consider the best approach for data preprocessing.
The team is presented with three options to build ML models for two use cases. The course explains why they would use AutoML, BigQuery ML, or custom training to achieve their objectives.
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This course explores the benefits of using Vertex AI Feature Store, how to improve the accuracy of ML models, and how to find which data columns make the most useful features. This course also includes content and labs on feature engineering using BigQuery ML, Keras, and TensorFlow.
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This course covers building ML models with TensorFlow and Keras, improving the accuracy of ML models and writing ML models for scaled use.
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The course begins with a discussion about data: how to improve data quality and perform exploratory data analysis. We describe Vertex AI AutoML and how to build, train, and deploy an ML model without writing a single line of code. You will understand the benefits of Big Query ML. We then discuss how to optimize a machine learning (ML) model and how generalization and sampling can help assess the quality of ML models for custom training.
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This course explores what ML is and what problems it can solve. The course also discusses best practices for implementing machine learning. You’re introduced to Vertex AI, a unified platform to quickly build, train, and deploy AutoML machine learning models.
The course discusses the five phases of converting a candidate use case to be driven by machine learning, and why it’s important to not skip them. The course ends with recognizing the biases that ML can amplify and how to recognize them.
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Earn a skill badge by completing the Create Conversational AI Agents with Dialogflow CX quest, where you will learn how to create a conversational virtual agent, including how to: define intents and entities, use versions and environments, create conversational branching, and use IVR features. 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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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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Welcome to "CCAI Operations and Implementation", the fourth course in the "Customer Experiences with Contact Center AI" series. In this course, learn some best practices for integrating conversational solutions with your existing contact center software, establishing a framework for human agent assistance, and implementing solutions securely and at scale. In this course, you'll be introduced to Agent Assist and the technology it uses so you can delight your customers with the efficiencies and accuracy of services provided when customers require human agents, connectivity protocols, APIs, and platforms which you can use to create an integration between your virtual agent and the services already established for your business, Dialogflow's Environment Management tool for deployment of different versions of your virtual agent for various purposes, compliance measures and regulations you should be aware of when bringing your virtual agent to production, and you'll be given tips from virtua…
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Welcome to "Virtual Agent Development in Dialogflow CX for Software Devs", the third course in the "Customer Experiences with Contact Center AI" series. In this course, learn how to develop more customized customer conversational solutions using Contact Center Artificial Intelligence (CCAI). In this course, you'll be introduced to more advanced and customized handling for virtual agent conversations that need to look up and convey dynamic data, and methods available to you for testing your virtual agent and logs which can be useful for understanding issues that arise.
This is an intermediate course, intended for learners with the following type of role:
Software developers: Codes computer software in a programming language (e.g., C++, Python, Javascript) and often using an SDK/API.
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Welcome to "Virtual Agent Development in Dialogflow CX for Citizen Devs", the second course in the "Customer Experiences with Contact Center AI" series.
In this course, learn how to develop customer conversational solutions using Contact Center Artificial Intelligence (CCAI). In this course, you'll be introduced to adding voice (telephony) as a communication channel to your virtual agent conversations using Dialogflow CX.
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Welcome to "CCAI Virtual Agent Development in Dialogflow ES for Software Developers", the third course in the "Customer Experiences with Contact Center AI" series.
In this course, learn to use additional features of Dialogflow ES for your virtual agent, create a Firestore instance to store customer data, and implement cloud functions that access the data. With the ability to read and write customer data, learner’s virtual agents are conversationally dynamic and able to defer contact center volume from human agents. You'll be introduced to methods for testing your virtual agent and logs which can be useful for understanding issues that arise. Lastly, learn about connectivity protocols, APIs, and platforms for integrating your virtual agent with services already established for your business.
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Welcome to "Virtual Agent Development in Dialogflow ES for Citizen Devs", the second course in the "Customer Experiences with Contact Center AI" series.
In this course, learn how to develop customer conversational solutions using Contact Center Artificial Intelligence (CCAI). You will use Dialogflow ES to create virtual agents and test them using the Dialogflow ES simulator. This course also provides best practices on developing virtual agents. You will also be introduced to adding voice (telephony) as a communication channel to your virtual agent conversations. Through a combination of presentations, demos, and hands-on labs, participants learn how to create virtual agents.
This is an intermediate course, intended for learners with the following types of roles:
Conversational designers: Designs the user experience of a virtual assistant. Translates the brand's business requirements into natural dialog flows.
Citizen developers: Creates new business applications fo…
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Welcome to "CCAI Conversational Design Fundamentals", the first course in the "Customer Experiences with Contact Center AI" series.
In this course, learn how to design customer conversational solutions using Contact Center Artificial Intelligence (CCAI). You will be introduced to CCAI and its three pillars (Dialogflow, Agent Assist, and Insights), and the concepts behind conversational experiences and how the study of them influences the design of your virtual agent. After taking this course you will be prepared to take your virtual agent design to the next level of intelligent conversation.
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Text Prompt Engineering Techniques introduces you to consider different strategic approaches & techniques to deploy when writing prompts for text-based generative AI tasks.
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This course will help ML Engineers, Developers, and Data Scientists implement Large Language Models for Generative AI use cases with Vertex AI. The first two modules of this course contain links to videos and prerequisite course materials that will build your knowledge foundation in Generative AI. Please do not skip these modules. The advanced modules in this course assume you have completed these earlier modules.
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Der Kurs „Generative KI kennenlernen – Agent Platform“ umfasst eine Reihe von Labs zur Verwendung von generativer KI in Google Cloud. In den Labs lernen Sie, wie Sie die Gemini-Modelle in der Agent Platform verwenden. Sie erfahren, wie Sie Prompts erstellen, Best Practices anwenden und die Modelle für Ideenfindung, Textklassifizierung, Textextraktion, Textzusammenfassung und mehr nutzen. Außerdem zeigen wir Ihnen, wie Sie ein Foundation Model durch benutzerdefiniertes Training in der Agent Platform abstimmen und auf einem Endpunkt in der Agent Platform bereitstellen.
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Dieser Kurs bietet eine Einführung in Vertex AI Studio, ein Tool für die Interaktion mit generativen KI-Modellen sowie das Prototyping von Geschäftsideen und ihre Umsetzung. Anhand eines eindrucksvollen Anwendungsfalls, ansprechender Lektionen und einer praktischen Übung lernen Sie den Lebenszyklus vom Prompt bis zum Produkt kennen und erfahren, wie Sie Vertex AI Studio für multimodale Gemini-Anwendungen, Prompt-Design, Prompt Engineering und Modellabstimmung einsetzen können. Ziel ist es, Ihnen aufzuzeigen, wie Sie das Potenzial von generativer KI in Ihren Projekten mit Vertex AI Studio ausschöpfen.
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In diesem Kurs erfahren Sie, wie Sie mithilfe von Deep Learning ein Modell zur Bilduntertitelung erstellen. Sie lernen die verschiedenen Komponenten eines solchen Modells wie den Encoder und Decoder und die Schritte zum Trainieren und Bewerten des Modells kennen. Nach Abschluss dieses Kurses haben Sie folgende Kompetenzen erworben: Erstellen eigener Modelle zur Bilduntertitelung und Verwenden der Modelle zum Generieren von Untertiteln
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Dieser Kurs bietet eine Einführung in die Transformer-Architektur und das BERT-Modell (Bidirectional Encoder Representations from Transformers). Sie lernen die Hauptkomponenten der Transformer-Architektur wie den Self-Attention-Mechanismus kennen und erfahren, wie Sie diesen zum Erstellen des BERT-Modells verwenden. Darüber hinaus werden verschiedene Aufgaben behandelt, für die BERT genutzt werden kann, wie etwa Textklassifizierung, Question Answering und Natural-Language-Inferenz. Der gesamte Kurs dauert ungefähr 45 Minuten.
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Dieser Kurs vermittelt Ihnen eine Zusammenfassung der Encoder-Decoder-Architektur, einer leistungsstarken und gängigen Architektur, die bei Sequenz-zu-Sequenz-Tasks wie maschinellen Übersetzungen, Textzusammenfassungen und dem Question Answering eingesetzt wird. Sie lernen die Hauptkomponenten der Encoder-Decoder-Architektur kennen und erfahren, wie Sie diese Modelle trainieren und bereitstellen können. Im dazugehörigen Lab mit Schritt-für-Schritt-Anleitung können Sie in TensorFlow von Grund auf einen Code für eine einfache Implementierung einer Encoder-Decoder-Architektur erstellen, die zum Schreiben von Gedichten dient.
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In diesem Kurs wird der Aufmerksamkeitsmechanismus vorgestellt. Dies ist ein leistungsstarkes Verfahren, das die Fokussierung neuronaler Netzwerke auf bestimmte Abschnitte einer Eingabesequenz ermöglicht. Sie erfahren, wie der Aufmerksamkeitsmechanismus funktioniert und wie Sie damit die Leistung verschiedener Machine Learning-Tasks wie maschinelle Übersetzungen, Zusammenfassungen von Texten und Question Answering verbessern können.
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In diesem Kurs werden Diffusion-Modelle vorgestellt, eine Gruppe verschiedener Machine Learning-Modelle, die kürzlich einige vielversprechende Fortschritte im Bereich Bildgenerierung gemacht haben. Diffusion-Modelle basieren auf physikalischen Konzepten der Thermodynamik und sind in den letzten Jahren in der Forschung und Industrie sehr beliebt geworden. Dabei stützen sich Diffusion-Modelle auf viele innovative Modelle und Tools zur Bildgenerierung in Google Cloud. In diesem Kurs werden Ihnen die theoretischen Grundlagen der Diffusion-Modelle erläutert und wie Sie diese Modelle über Vertex AI trainieren und bereitstellen können.
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Da die Nutzung von künstlicher Intelligenz und Machine Learning in Unternehmen weiter zunimmt, wird auch deren verantwortungsbewusste Entwicklung ein immer wichtigeres Thema. Dabei ist es für viele schwierig, die Überlegungen zur verantwortungsbewussten Anwendung von KI in die Praxis umzusetzen. Wenn Sie wissen möchten, wie sich die verantwortungsbewusste Anwendung von KI in die Praxis umsetzen, also operationalisieren lässt, finden Sie in diesem Kurs entsprechende Hilfestellungen.
In diesem Kurs erfahren Sie, wie dies mit Google Cloud heutzutage möglich ist, inklusive entsprechender Best Practices und Erkenntnisse. Es wird gezeigt, welches Framework Google Cloud bietet, um einen eigenen Ansatz für die verantwortungsbewusste Anwendung von KI zu entwickeln.
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Earn a skill badge by completing the Introduction to Generative AI, Introduction to Large Language Models and Introduction to Responsible AI courses. By passing the final quiz, you'll demonstrate your understanding of foundational concepts in generative AI.
A skill badge is a digital badge issued by Google Cloud in recognition of your knowledge of Google Cloud products and services. Share your skill badge by making your profile public and adding it to your social media profile.
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In diesem Einführungskurs im Microlearning-Format wird erklärt, was verantwortungsbewusste Anwendung von KI bedeutet, warum sie wichtig ist und wie Google dies in seinen Produkten berücksichtigt. Darüber hinaus werden die 7 KI-Grundsätze von Google behandelt.
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In diesem Einführungskurs im Microlearning-Format wird untersucht, was Large Language Models (LLM) sind, für welche Anwendungsfälle sie genutzt werden können und wie die LLM-Leistung durch Feinabstimmung von Prompts gesteigert werden kann. Darüber hinaus werden Tools von Google behandelt, die das Entwickeln eigener Anwendungen basierend auf generativer KI ermöglichen.
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In diesem Einführungskurs im Microlearning-Format wird erklärt, was generative KI ist, wie sie genutzt wird und wie sie sich von herkömmlichen Methoden für Machine Learning unterscheidet. Darüber hinaus werden Tools von Google behandelt, mit denen Sie eigene generative KI-Anwendungen entwickeln können.
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This course is part 1 of a 3-course series on Serverless Data Processing with Dataflow. In this first course, we start with a refresher of what Apache Beam is and its relationship with Dataflow. Next, we talk about the Apache Beam vision and the benefits of the Beam Portability framework. The Beam Portability framework achieves the vision that a developer can use their favorite programming language with their preferred execution backend. We then show you how Dataflow allows you to separate compute and storage while saving money, and how identity, access, and management tools interact with your Dataflow pipelines. Lastly, we look at how to implement the right security model for your use case on Dataflow.
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Incorporating machine learning into data pipelines increases the ability to extract insights from data. This course covers ways machine learning can be included in data pipelines on Google Cloud. For little to no customization, this course covers AutoML. For more tailored machine learning capabilities, this course introduces Notebooks and BigQuery machine learning (BigQuery ML). Also, this course covers how to productionalize machine learning solutions by using Vertex AI.
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This 1-week, accelerated on-demand course builds upon Google Cloud Platform Big Data and Machine Learning Fundamentals. Through a combination of video lectures, demonstrations, and hands-on labs, you'll learn to build streaming data pipelines using Google cloud Pub/Sub and Dataflow to enable real-time decision making. You will also learn how to build dashboards to render tailored output for various stakeholder audiences.
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In this intermediate course, you will learn to design, build, and optimize robust batch data pipelines on Google Cloud. Moving beyond fundamental data handling, you will explore large-scale data transformations and efficient workflow orchestration, essential for timely business intelligence and critical reporting.
Get hands-on practice using Dataflow for Apache Beam and Serverless for Apache Spark (Dataproc Serverless) for implementation, and tackle crucial considerations for data quality, monitoring, and alerting to ensure pipeline reliability and operational excellence. A basic knowledge of data warehousing, ETL/ELT, SQL, Python, and Google Cloud concepts is recommended.
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While the traditional approaches of using data lakes and data warehouses can be effective, they have shortcomings, particularly in large enterprise environments. This course introduces the concept of a data lakehouse and the Google Cloud products used to create one. A lakehouse architecture uses open-standard data sources and combines the best features of data lakes and data warehouses, which addresses many of their shortcomings.
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This course helps learners create a study plan for the PDE (Professional Data Engineer) certification exam. Learners explore the breadth and scope of the domains covered in the exam. Learners assess their exam readiness and create their individual study plan.
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Mit dem Skill-Logo Monitoring und Logging mit Google Cloud Observability weisen Sie Grundkenntnisse in folgenden Bereichen nach: Überwachen virtueller Maschinen in der Compute Engine, Einsetzen von Cloud Monitoring für Verwaltung mehrerer Projekte, Erweitern von Monitoring- und Logging-Funktionen zur Nutzung in Cloud Functions, Erstellen und Senden von benutzerdefinierten Anwendungsmesswerten und Konfigurieren von Cloud Monitoring-Benachrichtigungen auf der Grundlage benutzerdefinierter Messwerte.
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Mit dem Skill-Logo DevOps-Workflows in Google Cloud implementieren weisen Sie fortgeschrittene Kenntnisse in folgenden Bereichen nach: Git-Repositories mit Cloud Source Repositories erstellen, Deployments in der Google Kubernetes Engine (GKE) starten, verwalten und skalieren sowie CI/CD-Pipelines zur Automatisierung von Container-Image-Builds und GKE-Deployments entwerfen.
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Course two of the Architecting Hybrid Cloud with Anthos series prepares students to operate and observe Anthos environments. Through presentations and hands-on labs, participants explore adjusting existing clusters, setting up advanced traffic routing policies, securing communication across workloads, and observing clusters in Anthos. This course is a continuation of course one, Multi-Cluster, Multi-Cloud with Anthos, and assumes direct experience with the topics covered in that course.
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In this course you will learn the fundamentals of no-code app development and recognize use cases for no-code apps. The course provides an overview of the AppSheet no-code app development platform and its capabilities. You learn how to create an app with data from spreadsheets, create the app’s user experience using AppSheet views and publish the app to end users.
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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 self-paced training course gives participants broad study of security controls and techniques on Google Cloud. Through recorded lectures, demonstrations, and hands-on labs, participants explore and deploy the components of a secure Google Cloud solution, including Cloud Identity, Resource Manager, IAM, Virtual Private Cloud firewalls, Cloud Load Balancing, Cloud Peering, Cloud Interconnect, and VPC Service Controls.
This is the first course of the Security in Google Cloud series. After completing this course, enroll in the Security Best Practices in Google Cloud course.
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This course helps learners prepare for the Professional Cloud Security Engineer (PCSE) Certification exam. Learners will be exposed to and engage with exam topics through a series of lectures, diagnostic questions, and knowledge checks. After completing this course, learners will have a personalized workbook that will guide them through the rest of their certification readiness journey.
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Mit dem Skill-Logo zum Kurs Daten für ML-APIs in Google Cloud vorbereiten weisen Sie Grundkenntnisse in folgenden Bereichen nach: Bereinigen von Daten mit Dataprep von Trifacta, Ausführen von Datenpipelines in Dataflow, Erstellen von Clustern und Ausführen von Apache Spark-Jobs in Managed Service for Apache Spark sowie Aufrufen von ML-APIs, einschließlich der Cloud Natural Language API, Cloud Speech-to-Text API und Video Intelligence API.
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In many IT organizations, incentives are not aligned between developers, who strive for agility, and operators, who focus on stability. Site reliability engineering, or SRE, is how Google aligns incentives between development and operations and does mission-critical production support. Adoption of SRE cultural and technical practices can help improve collaboration between the business and IT. This course introduces key practices of Google SRE and the important role IT and business leaders play in the success of SRE organizational adoption.
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Mit dem Skill-Logo Kubernetes-Anwendungen in Google Cloud bereitstellen weisen Sie Kenntnisse in folgenden Bereichen nach: Konfigurieren und Erstellen von Docker-Container-Images, Erstellen und Verwalten von Google Kubernetes Engine-Clustern, Verwenden von kubectl für eine effiziente Clusterverwaltung und Bereitstellen von Kubernetes-Anwendungen mit leistungsfähigen Continuous Delivery-Abläufen.
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Mit dem Skill-Logo Serverlose Apps mit Firebase entwickeln weisen Sie Kenntnisse in den folgenden Bereichen nach: serverlose Webanwendungen mit Firebase entwickeln, Firestore für die Datenbankverwaltung verwenden, Bereitstellungsprozesse mit Cloud Build automatisieren und Google Assistant-Funktionen in Ihre Anwendungen integrieren.
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Mit dem Skill-Logo Serverlose Anwendungen in Cloud Run entwickeln weisen Sie Kenntnisse in den folgenden Bereichen nach: Cloud Run für Datenmanagement in Cloud Storage integrieren, mit Cloud Run und Pub/Sub die Architektur von asynchronen, ausfallsicheren Systemen erstellen, REST API-Gateways basierend auf Cloud Run aufbauen und Dienste auf Cloud Run entwickeln und bereitstellen.
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Course four of the Anthos series prepares students to consider multiple approaches for modernizing applications and services within Anthos environments. Topics include optimizing workloads on serverless platforms and migrating workloads to Anthos. This course is a continuation of course three, Anthos on Bare Metal, and assumes direct experience with the topics covered in that course.
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This course introduces you to fundamentals, practices, capabilities and tools applicable to modern cloud-native application development using Google Cloud Run. Through a combination of lectures, hands-on labs, and supplemental materials, you will learn how to on Google Cloud using Cloud Run.design, implement, deploy, secure, manage, and scale applications
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In this course, application developers learn how to design and develop cloud-native applications that seamlessly integrate components from the Google Cloud ecosystem. Through a combination of presentations, demos, and hands-on labs, participants learn how to create repeatable deployments by treating infrastructure as code, choose the appropriate application execution environment for an application, and monitor application performance.
Completing one version of each lab is required. Each lab is available in Node.js. In most cases, the same labs are also provided in Python or Java. You may complete each lab in whichever language you prefer.
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In this course, application developers learn how to design and develop cloud-native applications that seamlessly integrate managed services from Google Cloud. Through a combination of presentations, demos, and hands-on labs, participants learn how to develop more secure applications, implement federated identity management, and integrate application components by using messaging, event-driven processing, and API gateways. Completing one version of each lab is required. Each lab is available in Node.js. In most cases, the same labs are also provided in Python or Java. You may complete each lab in whichever language you prefer. This is the second course of the Developing Applications with Google Cloud series. After completing this course, enroll in the App Deployment, Debugging, and Performance course.
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In this course, application developers learn how to design and develop cloud-native applications that seamlessly integrate managed services from Google Cloud. Through a combination of presentations, demos, and hands-on labs, participants learn how to apply best practices for application development and use the appropriate Google Cloud storage services for object storage, relational data, caching, and analytics.
Completing one version of each lab is required. Each lab is available in Node.js. In most cases, the same labs are also provided in Python or Java. You may complete each lab in whichever language you prefer.
This is the first course of the Developing Applications with Google Cloud series. After completing this course, enroll in the Securing and Integrating Components of your Application course.
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In "Architecting with Google Kubernetes Engine- Workloads", you'll embark on a comprehensive journey into cloud-native application development. Throughout the learning experience, you'll explore Kubernetes operations, deployment management, GKE networking, and persistent storage. This is the first course of the Architecting with Google Kubernetes Engine series. After completing this course, enroll in the Architecting with Google Kubernetes Engine- Production course.
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In this course, "Architecting with Google Kubernetes Engine: Foundations," you get a review of the layout and principles of Google Cloud, followed by an introduction to creating and managing software containers and an introduction to the architecture of Kubernetes.
This is the first course of the Architecting with Google Kubernetes Engine series. After completing this course, enroll in the Architecting with Google Kubernetes Engine: Workloads course.
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Mit dem Skill-Logo zum Kurs Grundlegende Sicherheitsfunktionen in Google Cloud implementieren weisen Sie fortgeschrittene Kenntnisse in folgenden Bereichen nach: Erstellen und Zuweisen von Rollen mit Identity and Access Management (IAM); Erstellen und Verwalten von Dienstkonten; Herstellen einer privaten Verbindung zwischen Virtual Private Cloud-Netzwerken (VPC); Beschränken des Anwendungszugriffs mithilfe von Identity-Aware Proxy; Verwalten von Schlüsseln und verschlüsselten Daten mit Cloud Key Management Service (KMS); und Erstellen eines privaten Kubernetes-Clusters.
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If you want to take your Google Cloud networking skills to the next level, look no further. This course is composed of labs that cover real-life use cases and it will teach you best practices for overcoming common networking bottlenecks. From getting hands-on practice with testing and improving network performance, to integrating high-throughput VPNs and networking tiers, Network Performance and Optimization is an essential course for Google Cloud developers who are looking to double down on application speed and robustness.
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Sichern Sie sich ein Skill-Logo, indem Sie den Kurs Geschütztes Google Cloud-Netzwerk erstellen abschließen. Dabei lernen Sie verschiedene netzwerkbezogene Ressourcen kennen, mit denen Sie Ihre Anwendungen in Google Cloud erstellen, skalieren und schützen können.
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Welcome to the second course in the networking and Google Cloud series routing and addressing.
In this course, we'll cover the central routing and addressing concepts that are relevant to Google Cloud's networking capabilities.
Module one will lay the foundation by exploring network routing and addressing in Google Cloud, covering key building blocks such as routing IPv4, bringing your own IP addresses and setting up cloud DNS. In Module two will shift our focus to private connection options, exploring use cases and methods for accessing Google and other services privately using internal IP addresses.
By the end of this course, you'll have a solid grasp of how to effectively route and address your network traffic within Google Cloud.
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Networking in Google cloud is a 6 part course series. Welcome to the first course of our six part course series, Networking in Google Cloud: Fundamentals.
This course provides a comprehensive overview of core networking concepts, including networking fundamentals, virtual private clouds (VPCs), and the sharing of VPC networks. Additionally, the course covers network logging and monitoring techniques.
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Networking is a principle theme of cloud computing. It’s the underlying structure of Google Cloud, and it’s what connects all your resources and services to one another. This course will cover essential Google Cloud networking services and will give you hands-on practice with specialized tools for developing mature networks. From learning the ins-and-outs of VPCs, to creating enterprise-grade load balancers, Automate Deployment and Manage Traffic on a Google Cloud Network will give you the practical experience needed so you can start building robust networks right away.
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This course helps you structure your preparation for the Professional Cloud Engineer exam. You will learn about the Google Cloud domains covered by the exam and how to create a study plan to improve your domain knowledge.
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Erhalten Sie ein Skill-Logo, indem Sie den Kurs Cloud-Architektur: Entwerfen, umsetzen und verwalten abschließen. Dabei können Sie Fähigkeiten nachweisen, die für folgende Aufgaben nötig sind: eine öffentlich zugängliche Website mit Apache-Webservern bereitstellen, eine Compute Engine-VM mithilfe von Startscripts konfigurieren, sicheres RDP durch Nutzung von Firewallregeln und eines Windows-Bastion Hosts konfigurieren, ein Docker-Image in einem Kubernetes-Cluster bereitstellen und anschließend aktualisieren sowie eine Cloud SQL-Instanz erstellen und eine MySQL-Datenbank importieren. Diese Aufgabenreihe bietet eine gute Grundlage für bestimmte Themen, die Teil der Zertifizierungsprüfung zum Google Cloud Certified Professional Cloud Architect sind.
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Mit dem Skill-Logo zum Kurs Kostenoptimierung für die Google Kubernetes Engine weisen Sie fortgeschrittene Kenntnisse in folgenden Bereichen nach: Erstellen und Verwalten von Clustern für mehrere Mandanten, Überwachen der Ressourcenauslastung nach Namespace, Konfigurieren des Cluster- und Pod-Autoscalings zur Steigerung der Effizienz, Einrichten des Load Balancings zur optimalen Verteilung von Ressourcen und Implementieren von Aktivitäts- und Bereitschaftsprüfungen zum Sicherstellen von Anwendungszustand und Kosteneffektivität.
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Erhalten Sie ein Skill-Logo, indem Sie den Kurs Google Cloud-Netzwerk einrichten abschließen. Dabei lernen Sie, wie Sie grundlegende Netzwerkaufgaben in Google Cloud ausführen. Sie richten ein benutzerdefiniertes Netzwerk ein, fügen Firewallregeln für Subnetze hinzu, erstellen VMs und testen dann die Latenz bei der Kommunikation zwischen den VMs.
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This course equips students to build highly reliable and efficient solutions on Google Cloud using proven design patterns. It is a continuation of the Architecting with Google Compute Engine or Architecting with Google Kubernetes Engine courses and assumes hands-on experience with the technologies covered in either of those courses. Through a combination of presentations, design activities, and hands-on labs, participants learn to define and balance business and technical requirements to design Google Cloud deployments that are highly reliable, highly available, secure, and cost-effective.
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Learn how to use NotebookLM to create a personalized study guide for the Professional Cloud Architect certification exam.
You'll review NotebookLM features, create a notebook in NotebookLM, and learn how to use a study guide to practice for a certification exam.
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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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Cloud technology is a powerful asset, and when paired with data, it becomes a catalyst for innovation and enhanced customer experiences. Exploring Data Transformation with Google Cloud examines how organizations can leverage the cloud to make their data more accessible, actionable, and valuable.
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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Digital transformation is a critical journey for modern organizations, and establishing a strong baseline in cloud computing is the first step toward driving meaningful innovation. Digital Transformation with Google Cloud introduces the core technologies and strategic frameworks that help organizations modernize their operations. This course explores fundamental cloud concepts, global network infrastructure, and the shared responsibility model to help leaders navigate their path to the cloud with confidence.
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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Mit dem Skill-Logo Infrastruktur mit Terraform in Google Cloud erstellen weisen Sie fortgeschrittene Kenntnisse in folgenden Bereichen nach: Grundsätze von Infrastruktur als Code (IaC) unter Verwendung von Terraform, Bereitstellen und Verwalten von Google Cloud-Ressourcen mit Terraform-Konfigurationen, effektives Statusmanagement (lokal und remote) und die Modularisierung von Terraform-Code für Wiederverwendbarkeit und Organisation.
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Erhalten Sie ein Skill-Logo, indem Sie den Kurs Google Cloud-Netzwerk entwickeln abschließen. Dabei wird anhand verschiedener Aufgaben behandelt, wie Sie Anwendungen bereitstellen und beobachten, darunter: IAM-Rollen prüfen, den Zugriff auf Projekte ermöglichen/entfernen, VPC-Netzwerke erstellen, Compute Engine-VMs bereitstellen und beobachten, SQL-Abfragen schreiben, VMs in der Compute Engine bereitstellen und beobachten sowie Anwendungen mithilfe von Kubernetes und mehreren Deploymentmodellen bereitstellen.
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Erhalten Sie ein Skill-Logo, indem Sie den Kurs „Umgebung für die Anwendungsentwicklung in Google Cloud einrichten“ abschließen. Dabei lernen Sie, wie Sie eine speicherorientierte Cloud-Infrastruktur mithilfe der grundlegenden Funktionen der folgenden Technologien erstellen und verbinden: Cloud Storage, Identity and Access Management, Cloud Functions und Pub/Sub.
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Mit dem Skill-Logo zum Kurs Cloud Load Balancing in der Compute Engine implementieren weisen Sie Kenntnisse in folgenden Bereichen nach: virtuelle Maschinen in der Compute Engine erstellen und bereitstellen und Netzwerk- und Application Load Balancer konfigurieren.
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This course provides an introduction to using Terraform for Google Cloud. It enables learners to describe how Terraform can be used to implement infrastructure as code and to apply some of its key features and functionalities to create and manage Google Cloud infrastructure.
Learners will get hands-on practice building and managing Google Cloud resources using Terraform.
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Welcome to the two-part course on Logging, Monitoring, and Observability in Google Cloud. The core operations tools in Google Cloud break down into two major categories. The operations-focused components and the application performance management tools. This course, Logging and Monitoring in Google Cloud, covers the operations-focused components including Logging, Monitoring, and Service Monitoring. After taking this course, it is suggested that you complete part 2, Observability in Google Cloud, to learn about the available application performance management tools.
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Willkommen beim Kurs „Erste Schritte mit der Google Kubernetes Engine“. Sie interessieren sich für Kubernetes, eine Software-Ebene, die sich zwischen Ihren Anwendungen und der Hardwareinfrastruktur befindet? Dann sind Sie hier genau richtig! Die Google Kubernetes Engine bietet Ihnen Kubernetes als verwalteten Dienst in Google Cloud.
In diesem Kurs lernen Sie die Grundlagen der Google Kubernetes Engine (GKE) kennen und erfahren, wie Sie Anwendungen containerisieren und in Google Cloud ausführen. Er beginnt mit einer Einführung in Google Cloud, gefolgt von einem Überblick über Container und Kubernetes, die Kubernetes-Architektur sowie Kubernetes-Vorgänge.
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Dieser On-Demand-Intensivkurs bietet Teilnehmenden eine Einführung in die umfangreiche und flexible Infrastruktur und die Plattformdienste von Google Cloud. In Videovorträgen, Demos und praxisorientierten Labs lernen Teilnehmende Lösungselemente kennen und stellen sie bereit. Dazu gehören sichere Interconnect-Netzwerke, Load Balancing, Autoscaling, Automatisierung der Infrastruktur und verwaltete Dienste.
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Dieser On-Demand-Intensivkurs bietet Teilnehmenden eine Einführung in die umfangreiche und flexible Infrastruktur und die Plattformdienste von Google Cloud, mit Schwerpunkt auf der Compute Engine. In Videovorträgen, Demos und praxisorientierten Labs lernen die Teilnehmenden Lösungselemente kennen und stellen sie dann bereit. Dazu gehören Infrastrukturkomponenten wie Netzwerke, Systeme und Anwendungsdienste. Außerdem werden praktische Lösungen vorgestellt, beispielsweise für von Kundinnen und Kunden bereitgestellte Verschlüsselungsschlüssel, die Sicherheits- und Zugriffsverwaltung, Kontingente und Abrechnung sowie das Ressourcenmonitoring.
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Dieser On-Demand-Intensivkurs bietet Teilnehmenden eine Einführung in die umfangreiche und flexible Infrastruktur und die Plattformdienste von Google Cloud, mit Schwerpunkt auf der Compute Engine. In Videovorträgen, Demos und praxisorientierten Labs lernen Sie Lösungselemente kennen und stellen sie bereit. Dazu gehören Infrastrukturkomponenten wie Netzwerke, virtuelle Maschinen (VMs) und Anwendungsdienste. Darüber hinaus erfahren Sie, wie Sie über die Console und Cloud Shell mit Google Cloud arbeiten. Außerdem lernen Sie mehr über die Aufgaben eines Cloud Architects, über verschiedene Arten von Infrastrukturdesign und über die Konfiguration virtueller Netzwerke mithilfe von Virtual Private Cloud (VPC), Projekten, Netzwerken, Subnetzwerken, IP-Adressen, Routes und Firewallregeln.
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In „Google Cloud-Grundlagen: Kerninfrastruktur“ werden wichtige Konzepte und die Terminologie für die Arbeit mit Google Cloud vorgestellt. In Videos und praxisorientierten Labs werden viele Computing- und Speicherdienste von Google Cloud sowie wichtige Tools für die Ressourcen- und Richtlinienverwaltung präsentiert und miteinander verglichen.
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Dieser Kurs hilft Ihnen, sich strukturiert auf die Prüfung zum Associate Cloud Engineer vorzubereiten. Sie erfahren mehr über die in der Prüfung behandelten Google Cloud-Themen und wie Sie einen Lernplan zur Erweiterung Ihrer Kenntnisse erstellen.
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This course builds on some of the concepts covered in the earlier Google Sheets course.
In this course, you will learn how to apply and customize themes In Google Sheets, and explore conditional formatting options.
You will learn about some of Google Sheets’ advanced formulas and functions. You will explore how to create formulas using functions, and you will also learn how to reference and validate your data in a Google Sheet.
Spreadsheets can hold millions of numbers, formulas, and text. Making sense of all of that data can be difficult without a summary or visualization. This course explores the data visualization options in Google Sheets, such as charts and pivot tables.
Google Forms are online surveys used to collect data and provide the opportunity for quick data analysis. You will explore how Forms and Sheets work together by connecting collected Form data to a spreadsheet, or by creating a Form from an existing spreadsheet.
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In this course, we introduce you to Google Meet, Google’s video conference software included with Google Workspace.
You learn how to create and manage video conference meetings using Google Meet. You explore different ways to open Google Meet and add people to a video conference. You also learn how to join meetings from different sources like calendar events or meeting links.
We discuss how Google Meet can help you better communicate, exchange ideas, and share resources with your team wherever they are. You learn how to customize the Google Meet environment to fit your needs and how to effectively use chat messages during a video conference. You also explore different ways to share resources, such as by using calendar invites or attachments.
You learn about using host controls in Google Meet to manage participants and utilize interactive moderation features. You also learn how to record and live stream video conferences.
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With Google Slides, you can create and present professional presentations for sales, projects, training modules, and much more.
Google Slides presentations are stored safely in the cloud. You build presentations right in your web browser—no special software is required. Even better, multiple people can work on your slides at the same time, you can see people’s changes as they make them, and every change is automatically saved.
You will learn how to open Google Slides, create a blank presentation, and create a presentation from a template. You will explore themes, layout options, and how to add and format content, and speaker notes in your presentations.
You will learn how to enhance your slides by adding tables, images, charts, and more. You will also learn how to use slide transitions and object animations in your presentation for visual effects.
We will discuss how to organize slides and explore some of the options, including duplicating and ordering your slides, importi…
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In this course we will introduce you to Google Sheets, Google’s cloud-based spreadsheet software, included with Google Workspace.
With Google Sheets, you can create and edit spreadsheets directly in your web browser—no special software is required.
Multiple people can work simultaneously, you can see people’s changes as they make them, and every change is saved automatically.
You will learn how to open Google Sheets, create a blank spreadsheet, and create a spreadsheet from a template. You will add, import, sort, filter and format your data using Google Sheets and learn how to work across different file types.
Formulas and functions allow you to make quick calculations and better use your data. We will look at creating a basic formula, using functions, and referencing data. You will also learn how to add a chart to your spreadsheet.
Google Sheets spreadsheets are easy to share. We will look at the different ways you can share with others. We will also discuss how to track changes…
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With Google Docs, your documents are stored in the cloud, and you can access them from any computer or device. You create and edit documents right in your web browser; no special software is required. Even better, multiple people can work at the same time, you can see people’s changes as they make them, and every change is saved automatically.
In this course, you will learn how to open Google Docs, create and format a new document, and apply a template to a new document.
You will learn how to enhance your documents using a table of contents, headers and footers, tables, drawings, images, and more.
You will learn how to share your documents with others. We will discuss your sharing options and examine collaborator roles and permissions. You will learn how to manage versions of your documents.
Google Docs allows you to work in real time with others on the same document. You will learn how to create and manage comments and action items in your documents.
We will review a few of the G…
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Google Drive is Google’s cloud-based file storage service. Google Drive lets you keep all your work in one place, view different file formats without the need for additional software, and access your files from any device.
In this course, you will learn how to navigate your Google Drive. You will learn how to upload files and folders and how to work across file types. You will also learn how you can easily view, arrange, organize, modify, and remove files in Google Drive.
Google Drive includes shared drives. You can use shared drives to store, search, and access files with a team. You will learn how to create a new shared drive, add and manage members, and manage the shared drive content.
Google Workspace is synonymous with collaboration and sharing. You will explore the sharing options available to you in Google Drive, and you will learn about the various collaborator roles and permissions that can be assigned.
You’ll also explore ways to ensure consistency and save time…
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With Google Calendar, you can quickly schedule meetings and events and create tasks, so you always know what’s next. Google Calendar is designed for teams, so it’s easy to share your schedule with others and create multiple calendars that you and your team can use together.
In this course, you’ll learn how to create and manage Google Calendar events. You will learn how to update an existing event, delete and restore events, and search your calendar.
You will understand when to apply different event types such as tasks and appointment schedules.
You will explore the Google Calendar settings that are available for you to customize Google Calendar to suit your way of working.
During the course you will learn how to create additional calendars, share your calendars with others, and access other calendars in your organization.
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Gmail is Google’s cloud based email service that allows you to access your messages from any computer or device with just a web browser.
In this course, you’ll learn how to compose, send and reply to messages. You will also explore some of the common actions that can be applied to a Gmail message, and learn how to organize your mail using Gmail labels.
You will explore some common Gmail settings and features. For example, you will learn how to manage your own personal contacts and groups, customize your Gmail Inbox to suit your way of working, and create your own email signatures and templates.
Google is famous for search. Gmail also includes powerful search and filtering. You will explore Gmail’s advanced search and learn how to filter messages automatically.
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Earn a skill badge by completing the Configure your Workplace: Google Workspace for IT Admins quest, where you will get try out the Admin role for Workspace and learn to provision Groups, manage applications, security, and manage Meet. 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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Sichern Sie sich ein Skill-Logo, indem Sie den Kurs Cloud-Zusammenarbeit und Produktivitäts-Workflows implementieren abschließen. Darin lernen Sie die Plattform zur Zusammenarbeit von Google kennen und erfahren, wie Sie Gmail, Kalender, Meet, Drive, Sheets und AppSheet verwenden.
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Planning for a Google Workspace Deployment is the final course in the Google Workspace Administration series.
In this course, you will be introduced to Google's deployment methodology and best practices. You will follow Katelyn and Marcus as they plan for a Google Workspace deployment at Cymbal. They'll focus on the core technical project areas of provisioning, mail flow, data migration, and coexistence, and will consider the best deployment strategy for each area.
You will also be introduced to the importance of Change Management in a Google Workspace deployment, ensuring that users make a smooth transition to Google Workspace and gain the benefits of work transformation through communications, support, and training.
This course covers theoretical topics, and does not have any hands on exercises. If you haven’t already done so, please cancel your Google Workspace trial now to avoid any unwanted charges.
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In diesem Kurs wird vermittelt, wie Daten in einer Google Workspace-Umgebung verwaltet werden. Die Teilnehmenden lernen, wie sie Regeln zum Schutz vor Datenverlust in Gmail und Drive einrichten, um Datenlecks zu verhindern. Anschließend erfahren sie, wie sie Google Vault für die Aufbewahrung, die Sicherung und das Abrufen von Daten nutzen können. Als Nächstes lernen sie, wie sie Datenregionen und Exporteinstellungen so konfigurieren, dass sie den Vorschriften entsprechen. Außerdem wird gezeigt, wie Daten mithilfe von Labels klassifiziert werden können, um sie besser zu organisieren und zu schützen.
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In diesem Kurs lernen die Teilnehmenden, wie sie ihre Google Workspace-Umgebung schützen können. Sie implementieren strenge Passwortrichtlinien und die Bestätigung in zwei Schritten, um den Nutzerzugriff zu steuern. Anschließend lernen sie, wie sie mit dem Sicherheitsprüftool Risiken proaktiv erkennen und darauf reagieren können. Als Nächstes verwalten sie den Zugriff auf Drittanbieter-Anwendungen und Mobilgeräte, um für Sicherheit zu sorgen. Abschließend setzen die Teilnehmenden Maßnahmen für E-Mail-Sicherheit und Compliance um, die Unternehmensdaten schützen sollen.
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In diesem Kurs werden die Hauptdienste von Google Workspace umfassend behandelt. Teilnehmende lernen, wie sie Einstellungen für diese Dienste aktivieren, deaktivieren und konfigurieren können, darunter Gmail, Google Kalender, Google Drive, Google Meet, Google Chat und Google Docs. Anschließend erfahren sie, wie sie Gemini bereitstellen und verwalten können, um Nutzende zu unterstützen. Schließlich werden Anwendungsfälle für AppSheet und Apps Script untersucht, in denen Aufgaben automatisiert und die Funktionen von Google Workspace-Anwendungen erweitert werden.
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In diesem Kurs lernen Sie, wie Sie Nutzende und Ressourcen in Google Workspace verwalten. Die Teilnehmenden lernen, wie sie Organisationseinheiten so konfigurieren, dass sie den Anforderungen ihres Unternehmens entsprechen. Außerdem erfahren sie, wie sie verschiedene Arten von Google-Gruppen verwalten. Darüber hinaus wird die Verwaltung von Domaineinstellungen in Google Workspace behandelt. Schließlich wird gezeigt, wie Ressourcen in einer Google Workspace-Umgebung optimiert und strukturiert werden.
BI Reporting: Looker Visualization on BigQuery
Earned Okt 19, 2023 EDT
Einführung in das Cloud-Computing von Google: Cloud-Computing-Grundlagen
Earned Aug 7, 2023 EDT
Datenmodelle in Looker verwalten
Earned Aug 7, 2023 EDT
LookML-Objekte in Looker erstellen
Earned Aug 4, 2023 EDT
Data Catalog Fundamentals
Earned Aug 3, 2023 EDT
ML-Modelle mit BigQuery ML erstellen
Earned Aug 2, 2023 EDT
Informationen aus BigQuery-Daten ableiten
Earned Aug 2, 2023 EDT
Applying Machine Learning to your Data with Google Cloud
Earned Jul 31, 2023 EDT
Achieving Advanced Insights with BigQuery
Earned Jul 30, 2023 EDT
Creating New BigQuery Datasets and Visualizing Insights
Earned Jul 30, 2023 EDT
Exploring and Preparing your Data with BigQuery
Earned Jul 27, 2023 EDT
Modernizing Mainframe Applications with Google Cloud
Earned Jul 26, 2023 EDT
MySQL to Cloud SQL
Earned Jul 26, 2023 EDT
MySQL to Cloud Spanner
Earned Jul 26, 2023 EDT
Deploying and Managing Google Cloud VMware Engine
Earned Jul 26, 2023 EDT
Search with AI Applications
Earned Jul 26, 2023 EDT
Developing Data Models with LookML
Earned Jul 26, 2023 EDT
Analyzing and Visualizing Data in Looker
Earned Jul 26, 2023 EDT
Erweiterte LookML-Konzepte in Looker anwenden
Earned Jul 25, 2023 EDT
Understanding LookML in Looker
Earned Jul 25, 2023 EDT
Daten für Looker-Dashboards und ‑Berichte vorbereiten
Earned Jul 25, 2023 EDT
Generative AI Fundamentals
Earned Jul 25, 2023 EDT
Data Warehousing for Partners: BigQuery Extended Capabilities
Earned Jul 25, 2023 EDT
Data Warehousing for Partners: Streaming Analytics
Earned Jul 25, 2023 EDT
Data Warehousing for Partners: Analyze Data with Looker
Earned Jul 24, 2023 EDT
Data Warehousing for Partners: Stream Data with Pub/Sub
Earned Jul 24, 2023 EDT
Data Warehousing for Partners: Process Data with Dataflow
Earned Jul 24, 2023 EDT
Data Warehousing for Partners: Cloud Data Fusion Pipelines
Earned Jul 24, 2023 EDT
Data Warehousing for Partners: Process Data with Dataproc
Earned Jul 24, 2023 EDT
Data Warehousing for Partners: Migrate Data to BigQuery
Earned Jul 24, 2023 EDT
DevOps – Grundlagen
Earned Jul 23, 2023 EDT
CI/CD-Pipelines in Google Cloud umsetzen
Earned Jul 21, 2023 EDT
Grundlagen der Sicherheit und Identitätsverwaltung
Earned Jul 21, 2023 EDT
Architecting with Google Kubernetes Engine: Workloads - Locales
Earned Jul 21, 2023 EDT
Architecting with Google Kubernetes Engine: Foundations - Locales
Earned Jul 21, 2023 EDT
SAP on Google Cloud
Earned Jul 21, 2023 EDT
Hybrid Cloud Multi-Cluster with Anthos
Earned Jul 20, 2023 EDT
Hybrid Cloud Infrastructure Foundations with Anthos
Earned Jul 20, 2023 EDT
Architecting with Google Kubernetes Engine: Production
Earned Jul 20, 2023 EDT
Apigee X bereitstellen und verwalten
Earned Jul 20, 2023 EDT
On Premises Capacity Upgrade and Monitoring with Google Cloud's Apigee API Platform
Earned Jul 19, 2023 EDT
On Premises Management, Security, and Upgrade with Google Cloud's Apigee API Platform
Earned Jul 19, 2023 EDT
Upgrading and Monitoring the Apigee Hybrid API Platform
Earned Jul 18, 2023 EDT
Managing and Securing the Apigee Hybrid API Platform
Earned Jul 18, 2023 EDT
Architecting and Installing the Apigee Hybrid API Platform
Earned Jul 17, 2023 EDT
API Development with the Apigee API Platform
Earned Jul 17, 2023 EDT
API Security with the Apigee API Platform
Earned Jul 17, 2023 EDT
API Design and Fundamentals with the Apigee API Platform
Earned Jul 17, 2023 EDT
Cloud Foundations: Customer Onboarding Best Practices
Earned Jul 16, 2023 EDT
Machine Learning Operations (MLOps): Getting Started
Earned Jul 16, 2023 EDT
Recommendation Systems on Google Cloud
Earned Jul 16, 2023 EDT
Natural Language Processing on Google Cloud
Earned Jul 14, 2023 EDT
Computer Vision Fundamentals with Google Cloud
Earned Jul 14, 2023 EDT
BigQuery Fundamentals for Redshift Professionals
Earned Jul 13, 2023 EDT
Microsoft SQL Server to Cloud SQL
Earned Jul 13, 2023 EDT
Cloudera to Google Cloud
Earned Jul 13, 2023 EDT
Data Warehousing for Partners: Optimize in BigQuery
Earned Jul 13, 2023 EDT
Data Warehousing for Partners: Design in BigQuery
Earned Jul 13, 2023 EDT
Data Warehousing for Partners: Enable Google Cloud Customers
Earned Jul 13, 2023 EDT
Deploy and Monitor in Google Cloud for Azure Professionals
Earned Jul 13, 2023 EDT
Google Cloud Storage and Containers for Azure Professionals
Earned Jul 13, 2023 EDT
Google Cloud Compute and Scalability for Azure Professionals
Earned Jul 12, 2023 EDT
Google Cloud IAM and Networking for Azure Professionals
Earned Jul 12, 2023 EDT
Google Kubernetes Engine Best Practices: Security
Earned Jul 11, 2023 EDT
Mitigating Security Vulnerabilities on Google Cloud
Earned Jul 11, 2023 EDT
Security Best Practices in Google Cloud
Earned Jul 11, 2023 EDT
Production Machine Learning Systems
Earned Jul 10, 2023 EDT
Machine Learning in the Enterprise
Earned Jul 10, 2023 EDT
Feature Engineering
Earned Jul 10, 2023 EDT
Build, Train and Deploy ML Models with Keras on Google Cloud
Earned Jul 10, 2023 EDT
Launching into Machine Learning
Earned Jul 10, 2023 EDT
How Google Does Machine Learning
Earned Jul 9, 2023 EDT
DEPRECATED Create Conversational AI Agents with Dialogflow CX
Earned Jul 7, 2023 EDT
Automate Interactions with Contact Center AI
Earned Jul 6, 2023 EDT
CCAI Operations and Implementation
Earned Jul 5, 2023 EDT
Virtual Agent Development in Dialogflow CX for Software Devs
Earned Jul 5, 2023 EDT
Virtual Agent Development in Dialogflow CX for Citizen Devs
Earned Jul 5, 2023 EDT
Virtual Agent Development in Dialogflow ES for Software Devs
Earned Jul 5, 2023 EDT
Virtual Agent Development in Dialogflow ES for Citizen Devs
Earned Jul 5, 2023 EDT
Contact Center AI: Conversational Design Fundamentals
Earned Jul 5, 2023 EDT
Text Prompt Engineering Techniques
Earned Jul 5, 2023 EDT
Implementing Generative AI with Vertex AI
Earned Jul 4, 2023 EDT
Generative KI kennenlernen – Agent Platform
Earned Jul 4, 2023 EDT
Einführung in Vertex AI Studio
Earned Jul 4, 2023 EDT
Modelle zur Bilduntertitelung erstellen
Earned Jul 4, 2023 EDT
Transformer-Modelle und BERT-Modell
Earned Jul 4, 2023 EDT
Encoder-Decoder-Architektur
Earned Jul 4, 2023 EDT
Aufmerksamkeitsmechanismus
Earned Jul 4, 2023 EDT
Einstieg in die Bildgenerierung
Earned Jul 4, 2023 EDT
Verantwortungsbewusste Anwendung von KI: KI-Grundsätze in Google Cloud anwenden
Earned Jul 4, 2023 EDT
Generative AI Fundamentals
Earned Jul 4, 2023 EDT
Einführung in die verantwortungsbewusste Anwendung von KI
Earned Jul 4, 2023 EDT
Einführung in Large Language Models
Earned Jul 4, 2023 EDT
Einführung in generative KI
Earned Jul 4, 2023 EDT
Serverless Data Processing with Dataflow: Foundations
Earned Jul 4, 2023 EDT
Smart Analytics, Machine Learning, and AI on Google Cloud
Earned Jul 4, 2023 EDT
Building Resilient Streaming Systems on Google Cloud Platform
Earned Jul 4, 2023 EDT
Build Batch Data Pipelines on Google Cloud
Earned Jul 3, 2023 EDT
Build Data Lakes and Data Warehouses on Google Cloud
Earned Jul 3, 2023 EDT
Preparing for your Professional Data Engineer Journey
Earned Jul 3, 2023 EDT
Monitoring und Logging mit Google Cloud Observability
Earned Jul 3, 2023 EDT
DevOps-Workflows in Google Cloud implementieren
Earned Jul 2, 2023 EDT
DEPRECATED Cloud Operations and Service Mesh with Anthos
Earned Jul 2, 2023 EDT
Building No-Code Apps with AppSheet: Foundations
Earned Jun 30, 2023 EDT
[DEPRECATED] Google Cloud Big Data and Machine Learning Fundamentals
Earned Jun 30, 2023 EDT
Managing Security in Google Cloud
Earned Jun 28, 2023 EDT
Preparing for Your Professional Cloud Security Engineer Journey
Earned Jun 28, 2023 EDT
Daten für ML-APIs in Google Cloud vorbereiten
Earned Jun 27, 2023 EDT
Developing a Google SRE Culture
Earned Jun 27, 2023 EDT
Kubernetes-Anwendungen in Google Cloud bereitstellen
Earned Jun 27, 2023 EDT
Serverlose Apps mit Firebase entwickeln
Earned Jun 27, 2023 EDT
Serverlose Anwendungen in Cloud Run entwickeln
Earned Jun 26, 2023 EDT
Hybrid Cloud Modernizing Applications with Anthos
Earned Jun 26, 2023 EDT
Application Development with Cloud Run
Earned Jun 26, 2023 EDT
App Deployment, Debugging, and Performance
Earned Jun 26, 2023 EDT
Securing and Integrating Components of your Application
Earned Jun 26, 2023 EDT
Getting Started With Application Development
Earned Jun 25, 2023 EDT
Architecting with Google Kubernetes Engine: Workloads
Earned Jun 23, 2023 EDT
Architecting with Google Kubernetes Engine: Foundations
Earned Jun 23, 2023 EDT
Grundlegende Sicherheitsfunktionen in Google Cloud implementieren
Earned Jun 23, 2023 EDT
DEPRECATED Network Performance and Optimization
Earned Jun 22, 2023 EDT
Geschütztes Google Cloud-Netzwerk erstellen
Earned Jun 22, 2023 EDT
Networking in Google Cloud: Routing and Addressing
Earned Jun 22, 2023 EDT
Networking in Google Cloud: Fundamentals
Earned Jun 22, 2023 EDT
Automate Deployment and Manage Traffic on a Google Cloud Network
Earned Jun 22, 2023 EDT
Preparing for Your Professional Cloud Network Engineer Journey
Earned Jun 21, 2023 EDT
Cloud-Architektur: Entwerfen, umsetzen und verwalten
Earned Jun 21, 2023 EDT
Kostenoptimierung für die Google Kubernetes Engine
Earned Jun 20, 2023 EDT
Google Cloud-Netzwerk einrichten
Earned Jun 19, 2023 EDT
Reliable Google Cloud Infrastructure: Design and Process
Earned Jun 19, 2023 EDT
Build a Certification Study Guide: PCA Exam Prep
Earned Jun 19, 2023 EDT
Scaling with Google Cloud Operations
Earned Jun 18, 2023 EDT
Modernize Infrastructure and Applications with Google Cloud
Earned Jun 18, 2023 EDT
Exploring Data Transformation with Google Cloud
Earned Jun 18, 2023 EDT
Digital Transformation with Google Cloud
Earned Jun 18, 2023 EDT
Infrastruktur mit Terraform in Google Cloud erstellen
Earned Jun 17, 2023 EDT
Google Cloud-Netzwerk entwickeln
Earned Jun 17, 2023 EDT
Umgebung für die Anwendungsentwicklung in Google Cloud einrichten
Earned Jun 16, 2023 EDT
Cloud Load Balancing in der Compute Engine implementieren
Earned Jun 16, 2023 EDT
Getting Started with Terraform for Google Cloud
Earned Jun 16, 2023 EDT
Logging and Monitoring in Google Cloud
Earned Jun 16, 2023 EDT
Erste Schritte mit der Google Kubernetes Engine
Earned Jun 15, 2023 EDT
Elastische Google Cloud-Infrastruktur: Skalierung und Automatisierung
Earned Jun 15, 2023 EDT
Einführung in die Google Cloud-Infrastruktur: Hauptdienste
Earned Jun 14, 2023 EDT
Einführung in die Google Cloud-Infrastruktur: Grundlagen
Earned Jun 14, 2023 EDT
Google Cloud-Grundlagen: Kerninfrastruktur
Earned Jun 13, 2023 EDT
Vorbereitung auf die Prüfung zum Associate Cloud Engineer
Earned Jun 12, 2023 EDT
Google Sheets - Advanced Topics
Earned Jun 9, 2023 EDT
Google Meet
Earned Jun 9, 2023 EDT
Google Slides
Earned Jun 9, 2023 EDT
Google Sheets
Earned Jun 9, 2023 EDT
Google Docs
Earned Jun 9, 2023 EDT
Google Drive
Earned Jun 9, 2023 EDT
Google Calendar
Earned Jun 8, 2023 EDT
Gmail
Earned Jun 8, 2023 EDT
Configure your Workplace: Google Workspace for IT Admins
Earned Jun 8, 2023 EDT
Cloud-Zusammenarbeit und Produktivitäts-Workflows implementieren
Earned Jun 8, 2023 EDT
DEPRECATED Planning for a Google Workspace Deployment
Earned Jun 7, 2023 EDT
Data Governance bei der Google Workspace-Nutzung
Earned Jun 6, 2023 EDT
Sicherheit in Google Workspace
Earned Jun 6, 2023 EDT
Google Workspace-Hauptdienste
Earned Jun 6, 2023 EDT
Nutzer- und Ressourcenverwaltung in Google Workspace
Earned Jun 6, 2023 EDT