Create media search and media recommendations applications with AI Applications
Earned Okt 8, 2025 EDT
Configure AI Applications to optimize search results
Earned Okt 8, 2025 EDT
Create and maintain Vertex AI Search data stores
Earned Okt 7, 2025 EDT
Create Data Stores for Gen AI Applications
Earned Okt 7, 2025 EDT
Recommendations with AI Applications
Earned Okt 6, 2025 EDT
Introduction to NotebookLM
Earned Okt 6, 2025 EDT
Improve Vertex AI Search and Gemini Enterprise Search Results
Earned Okt 6, 2025 EDT
Vertex AI Search and Gemini Enterprise Analytics
Earned Okt 5, 2025 EDT
Vertex AI Search and Gemini Enterprise UI Configurations
Earned Okt 5, 2025 EDT
Build search and recommendations applications with AI Applications
Earned Okt 5, 2025 EDT
Introduction to AI Applications
Earned Okt 5, 2025 EDT
Getting Started with Terraform for Google Cloud
Earned Jul 21, 2025 EDT
Wissensaustausch mit Agentspace beschleunigen
Earned Mär 5, 2025 EST
Gemini für Cloud Architects
Earned Feb 8, 2025 EST
Gemini für DevOps Engineers
Earned Feb 8, 2025 EST
Introduction to Security in the World of AI
Earned Feb 7, 2025 EST
GCC Tech Learning Packs - TPU/GPU - SME Academy
Earned Jan 29, 2025 EST
Verantwortungsbewusste Anwendung von KI für Entwickler: Datenschutz und Sicherheit
Earned Jan 27, 2025 EST
Verantwortungsbewusste Anwendung von KI für Entwickler: Interpretierbarkeit und Transparenz
Earned Jan 27, 2025 EST
Verantwortungsbewusste Anwendung von KI für Entwickler: Fairness und Bias
Earned Jan 27, 2025 EST
Auf generativer KI basierende Anwendungen in Google Cloud entwickeln
Earned Jan 27, 2025 EST
ML Pipelines on Google Cloud
Earned Jan 25, 2025 EST
Machine Learning Operations (MLOps) mit Vertex AI: Modellbewertung
Earned Jan 25, 2025 EST
Machine Learning Operations (MLOps) für generative KI
Earned Jan 24, 2025 EST
Production Machine Learning Systems
Earned Jan 24, 2025 EST
Feature Engineering
Earned Jan 23, 2025 EST
Verantwortungsbewusste Anwendung von KI: KI-Grundsätze in Google Cloud anwenden
Earned Jan 21, 2025 EST
Working with Notebooks in Vertex AI
Earned Jan 16, 2025 EST
Einführung in KI und maschinelles Lernen in Google Cloud
Earned Jan 15, 2025 EST
Professional Machine Learning Engineer Study Guide
Earned Jan 14, 2025 EST
Umgebung für die Anwendungsentwicklung in Google Cloud einrichten
Earned Jun 3, 2024 EDT
Preparing for your Professional Cloud Architect Journey
Earned Jun 2, 2024 EDT
Einführung in die verantwortungsbewusste Anwendung von KI
Earned Mai 30, 2024 EDT
DEPRECATED Deploy Google Cloud Framework Data Foundation for SAP
Earned Jun 14, 2023 EDT
Transformer-Modelle und BERT-Modell
Earned Mai 13, 2023 EDT
Aufmerksamkeitsmechanismus
Earned Mai 12, 2023 EDT
Einführung in Large Language Models
Earned Mai 12, 2023 EDT
Einführung in generative KI
Earned Mai 12, 2023 EDT
How Google Does Machine Learning
Earned Apr 20, 2023 EDT
Create and Manage AlloyDB Instances
Earned Apr 12, 2023 EDT
Architecting with Google Kubernetes Engine: Foundations
Earned Feb 21, 2023 EST
Smart Analytics, Machine Learning, and AI on Google Cloud
Earned Jan 29, 2023 EST
Build Streaming Data Pipelines on Google Cloud
Earned Jan 29, 2023 EST
Build Batch Data Pipelines on Google Cloud
Earned Jan 29, 2023 EST
Build Data Lakes and Data Warehouses on Google Cloud
Earned Jan 27, 2023 EST
Preparing for Your Professional Cloud Security Engineer Journey
Earned Jan 8, 2023 EST
Serverless Data Processing with Dataflow: Foundations
Earned Jul 22, 2022 EDT
Google Cloud Big Data and Machine Learning Fundamentals
Earned Jul 10, 2022 EDT
Preparing for your Professional Data Engineer Journey
Earned Jul 9, 2022 EDT
Elastic Google Cloud Infrastructure: Scaling and Automation
Earned Jun 17, 2022 EDT
Essential Google Cloud Infrastructure: Core Services
Earned Jun 17, 2022 EDT
Kostenoptimierung für die Google Kubernetes Engine
Earned Jun 13, 2022 EDT
Google Cloud-Netzwerk einrichten
Earned Jun 12, 2022 EDT
Google Cloud-Netzwerk entwickeln
Earned Jun 12, 2022 EDT
Logging and Monitoring in Google Cloud
Earned Jun 10, 2022 EDT
Reliable Google Cloud Infrastructure: Design and Process
Earned Jun 9, 2022 EDT
Preparing for your Professional Cloud Architect Journey
Earned Jun 9, 2022 EDT
Essential Google Cloud Infrastructure: Foundation
Earned Jun 8, 2022 EDT
Google Cloud-Grundlagen: Kerninfrastruktur
Earned Jun 8, 2022 EDT
Getting Started with Google Kubernetes Engine
Earned Jun 8, 2022 EDT
Umgebung für die Anwendungsentwicklung in Google Cloud einrichten
Earned Mai 29, 2022 EDT
Curious about the powerful hardware behind AI? This course breaks down performance-optimized AI computers, showing you why they're so important. We'll explore how CPUs, GPUs, and TPUs make AI tasks super fast, what makes each one unique, and how AI software gets the most out of them. By the end, you'll know exactly how to pick the right compute for your AI projects, helping you make smart choices for your AI workkoads.
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Ready to get started with AI Hypercomputers? This course makes it easy! We'll cover the basics of what they are and how they help AI with AI workloads. You'll learn about the different components inside a hypercomputer, like GPUs, TPUs, and CPUs, and discover how to pick the right deployment approach for your needs.
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Complete the Extend Gemini Enterprise Assistant Capabilities skill badge to demonstrate your ability to extend Gemini Enterprise assistant's capabilities with actions, grounding with Google Search, and a conversational 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 assessment challenge lab, to receive a skill badge that you can share with your network.
When you complete this course, you can earn the badge displayed here and claim it on Credly! Boost your cloud career by showing the world the skills you have developed!
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Complete the Create media search and media recommendations applications with AI Applications skill badge to demonstrate your ability to create, configure, and access media search and recommendations applications using AI Applications.
Please note that AI Applications was previously named Agent Builder, so you may encounter this older name within the lab content.
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 assessment challenge lab, to receive a skill badge that you can share with your network.
When you complete this course, you can earn the badge displayed here and claim it on Credly! Boost your cloud career by showing the world the skills you have developed!
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Complete the Configure AI Applications to optimize search results skill badge to demonstrate your proficiency in configuring search results from AI Applications. You will be tasked with implementing search serving controls to boost and bury results, filter entries from search results and display metadata in your search interface.
Please note that AI Applications was previously named Agent Builder, so you may encounter this older name within the lab content.
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 assessment challenge lab, to receive a skill badge that you can share with your network.
When you complete this course, you can earn the badge displayed here and claim it on Credly! Boost your cloud career by showing the world the skills you have developed!
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Complete the Create and maintain Vertex AI Search data stores skill badge to demonstrate your proficiency in building various types of data stores used in Vertex AI Search applications.
A skill badge is an exclusive digital badge issued by Google Cloud in recognition of your proficiency with Google Cloud products and services and tests your ability to apply your knowledge in an interactive hands-on environment. Complete the assessment challenge lab, to receive a skill badge that you can share with your network.
When you complete this course, you can earn the badge displayed here and claim it on Credly! Boost your cloud career by showing the world the skills you have developed!
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Data stores represent a simple way to make content available to many types of generative AI applications, including search applications, recommendations engines, Gemini Enterprise apps, Agent Development Kit agents, and apps built with Google Gen AI or LangChain SDKs. Connect data from many sources include Cloud Storage, Google Drive, chat apps, mail apps, ticketing systems, third-party file storage providers, Salesforce, and many more.
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Do you want to keep your users engaged by suggesting content they'll love?
This course equips you with the skills to build a cutting-edge recommendations app using your own data with no prior machine learning knowledge. You learn to leverage AI Applications to build recommendation applications so that audiences can discover more personalized content, like what to watch or read next, with Google-quality results customized using optimization objectives.
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NotebookLM is an AI-powered collaborator that helps you do your best thinking. After uploading your documents, NotebookLM becomes an instant expert in those sources so you can read, take notes, and collaborate with it to refine and organize your ideas.
NotebookLM Pro gives you everything already included with NotebookLM, as well as higher utilization limits, access to premium features, and additional sharing options and analytics.
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If you've worked with data, you know that some data is more reliable than other data.
In this course, you'll learn a variety of techniques to present the most reliable or useful results to your users. Create serving controls to boost or bury search results. Rank search results to ensure that each query is answered by the most relevant data. If needed, tune your search engine. Learn to measure search results to ensure your search applications deliver the best possible results to each user.
(Please note Gemini Enterprise was previously named Google Agentspace, there may be references to the previous product name in this course.)
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AI Applications provides built-in analytics for your Vertex AI Search and Gemini Enterprise apps. Learn what metrics are tracked and how to view them in this course.
(Please note Gemini Enterprise was previously named Google Agentspace, there may be references to the previous product name in this course.)
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Initial deployment of Vertex AI Search and Gemini Enterprise apps takes only a few clicks, but getting the configurations right can elevate a deployment from a basic off-the-shelf app to an excellent custom search or recommendations experience.
In this course, you'll learn more about the many ways you can customize and improve search, recommendations, and Gemini Enterprise apps.
(Please note Gemini Enterprise was previously named Google Agentspace, there may be references to the previous product name in this course.)
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Complete the Build search and recommendations AI Applications skill badge to demonstrate your proficiency in deploying search and recommendation applications through AI Applications. Additionally, emphasis is placed on constructing a tailored Q&A system utilizing data stores.
Please note that AI Applications was previously named Agent Builder, so you may encounter this older name within the lab content.
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 assessment challenge lab, to receive a skill badge that you can share with your network.
When you complete this course, you can earn the badge displayed here and claim it on Credly! Boost your cloud career by showing the world the skills you have developed!
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This course introduces AI Applications. You will learn about the types of apps that you can create using AI Applications, the high-level steps that its data stores automate for you, and what advanced features can be enabled for Search apps.
(Please note Gemini Enterprise was previously named Google Agentspace, there may be references to the previous product name in this course.)
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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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Agentspace, ein Unternehmenstool, mit dem Mitarbeiter bestimmte Informationen in Dokumentenspeichern, E‑Mails, Chats, Ticketsystemen und anderen Datenquellen über eine einzige Suchleiste finden können, vereint das Fachwissen von Google in den Bereichen Suche und KI. Der Agentspace-Assistent kann auch beim Brainstorming, der Recherche oder der Strukturierung von Dokumenten unterstützen und zum Beispiel Kollegen zu einem Kalendertermin einladen, um Wissensarbeit sowie die Zusammenarbeit zu beschleunigen.
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In diesem Kurs erfahren Sie, wie Gemini, ein auf generativer KI basierendes Produkt von Google Cloud, Administratoren bei der Bereitstellung von Infrastruktur unterstützt. Sie lernen die Prompts kennen, mit denen Gemini Infrastruktur erklären, GKE-Cluster bereitstellen und eine bestehende Infrastruktur aktualisieren kann. In einem praxisorientierten Lab können Sie sich davon überzeugen, wie die GKE-Bereitstellung durch Gemini verbessert wird.
Duet AI wurde umbenannt in Gemini, unser Modell der nächsten Generation.
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In diesem Kurs erfahren Sie, wie Gemini, ein auf generativer KI basierendes Produkt von Google Cloud, Engineers bei der Verwaltung von Infrastruktur unterstützt. Sie lernen die Prompts kennen, mit denen Gemini dazu gebracht werden kann, Anwendungslogs zu suchen und zu verstehen, einen GKE-Cluster zu erstellen und Informationen zur Erstellung einer Build-Umgebung zu liefern. In einem praxisorientierten Lab können Sie sich davon überzeugen, wie der DevOps-Workflow durch Gemini verbessert wird.
Duet AI wurde umbenannt in Gemini, unser Modell der nächsten Generation.
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Artificial Intelligence (AI) offers transformative possibilities, but it also introduces new security challenges. This course equips security and data protection leaders with strategies to securely manage AI within their organizations. Learn a framework for proactively identifying and mitigating AI-specific risks, protecting sensitive data, ensuring compliance, and building a resilient AI infrastructure. Pick use cases from four different industries to explore how these strategies apply in real-world scenarios.
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This is an opportunity for Technical Cloud Googlers to enhance their TPU/GPU skills and knowledge through a deep dive program covering the following focus areas/ individual tracks:
TPU/GPU Networking
GKE Expertise
Slurm Expertise
TPU/GPU Storage Best Practices
Common transformer/LLM frameworks (Huggingface/triton/etc)
Monitoring and logging for TPU/GPU
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In diesem Kurs werden wichtige Themen zu Datenschutz und Sicherheit beim Einsatz von künstlicher Intelligenz vorgestellt. Dabei lernen Sie, wie Sie mit Google Cloud-Produkten und Open-Source-Tools empfohlene Vorgehensweisen im Zusammenhang mit Datenschutz und Sicherheit beim Einsatz von KI umsetzen.
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In diesem Kurs werden Konzepte in Bezug auf die Interpretierbarkeit und Transparenz von künstlicher Intelligenz vorgestellt. Sie erfahren, warum die Transparenz der KI für Entwickler-Teams wichtig ist. Dabei lernen Sie praktische Techniken und Tools kennen, mit denen Sie sowohl die Interpretierbarkeit als auch die Transparenz von Daten und KI-Modellen optimieren können.
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In diesem Kurs werden Konzepte für die verantwortungsbewusste Anwendung von KI und KI-Grundsätze vorgestellt. Es werden Techniken behandelt, wie Sie Fairness und Verzerrung (Bias) in der Praxis erkennen sowie Verzerrung in KI- und ML-Anwendungen reduzieren können. Dabei lernen Sie, wie Sie mit Google Cloud-Produkten und Open-Source-Tools Best Practices für eine verantwortungsbewusste Anwendung von KI umsetzen.
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Mit auf generativer KI basierenden Anwendungen, kurz GenAI-Anwendungen, werden Nutzerinteraktionen möglich, die es vor Large Language Models (LLMs) kaum gab. Wie können Sie als Anwendungsentwickler mit generativer KI interaktive, leistungsstarke Anwendungen in Google Cloud erstellen?
In diesem Kurs lernen Sie etwas über Anwendungen, die auf generativer KI basieren, und erfahren, wie Sie Prompt-Design und Retrieval-Augmented Generation (RAG) nutzen können, um mit LLMs leistungsstarke Anwendungen zu entwickeln. Wir stellen Ihnen eine produktionsreife Architektur für auf generativer KI basierende Anwendungen vor und Sie erstellen eine Chat-Anwendung auf der Basis von LLMs und RAG.
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In this course, you will be learning from ML Engineers and Trainers who work with the state-of-the-art development of ML pipelines here at Google Cloud. The first few modules will cover about TensorFlow Extended (or TFX), which is Google’s production machine learning platform based on TensorFlow for management of ML pipelines and metadata. You will learn about pipeline components and pipeline orchestration with TFX. You will also learn how you can automate your pipeline through continuous integration and continuous deployment, and how to manage ML metadata. Then we will change focus to discuss how we can automate and reuse ML pipelines across multiple ML frameworks such as tensorflow, pytorch, scikit learn, and xgboost. You will also learn how to use another tool on Google Cloud, Cloud Composer, to orchestrate your continuous training pipelines. And finally, we will go over how to use MLflow for managing the complete machine learning life cycle.
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Dieser Kurs gibt Machine-Learning-Anwendern alle grundlegenden Tools, Techniken und Best Practices zur Bewertung von generativen und prädiktiven KI-Modellen an die Hand. Die Modellbewertung ist ein wichtiger Schritt, bei dem geprüft wird, ob ML-Systeme in der Produktion zuverlässige, genaue und leistungsstarke Ergebnisse erzielen.
Die Teilnehmer erwerben fundierte Kenntnisse über verschiedene Bewertungsmesswerte und -methoden und lernen, sie auf unterschiedliche Modelltypen und Aufgaben anzuwenden. Im Kurs wird schwerpunktmäßig auf die besonderen Herausforderungen generativer KI-Modelle eingegangen und es werden Strategien vorgestellt, wie sich diese effektiv bewältigen lassen. Die Teilnehmer lernen auf der Plattform Vertex AI von Google Cloud, robuste Bewertungsprozesse zur Auswahl, Optimierung und kontinuierlichen Überwachung des Modells zu implementieren.
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Dieser Kurs vermittelt Ihnen das Wissen und die nötigen Tools, um die speziellen Herausforderungen zu erkennen, mit denen MLOps-Teams bei der Bereitstellung und Verwaltung von Modellen basierend auf generativer KI konfrontiert sind. Sie erfahren, wie KI-Teams durch Vertex AI dabei unterstützt werden, MLOps-Prozesse zu optimieren und mit Projekten erfolgreich zu sein, in denen generative KI zum Einsatz kommt.
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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 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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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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This course is an introduction to Vertex AI Notebooks, which are Jupyter notebook-based environments that provide a unified platform for the entire machine learning workflow, from data preparation to model deployment and monitoring. The course covers the following topics: (1) The different types of Vertex AI Notebooks and their features and (2) How to create and manage Vertex AI Notebooks.
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In diesem Kurs lernen Sie die KI- und ML-Angebote von Google Cloud für Projekte mit prädiktiver und generativer KI kennen. Dabei werden die Technologien, Produkte und Tools vorgestellt, die für den gesamten Lebenszyklus der Datenaufbereitung für KI verfügbar sind. Der Kurs umfasst KI‑Grundlagen, ‑Entwicklung und ‑Lösungen. Data Scientists, KI-Entwickler und ML-Engineers sollen in diesem Kurs ihre Fähigkeiten und Kenntnisse durch ansprechende Lernangebote sowie praxisorientierte Übungen erweitern.
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This course helps learners create a study plan for the PMLE (Professional Machine Learning 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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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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This course helps learners create a study plan for the PCA (Professional Cloud Architect) 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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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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Earn a skill badge by completing the Deploy Google Cloud Framework Data Foundation for SAP quest. 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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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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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 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 Anwendungen basierend auf generativer KI entwickeln können.
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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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Complete the introductory Create and Manage AlloyDB Instances skill badge to demonstrate skills in the following: performing core AlloyDB operations and tasks, migrating to AlloyDB from PostgreSQL, administering an AlloyDB database, and accelerating analytical queries using the AlloyDB Columnar Engine.
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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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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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In this course you will get hands-on in order to work through real-world challenges faced when building streaming data pipelines. The primary focus is on managing continuous, unbounded data with Google Cloud products.
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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 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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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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This course introduces the Google Cloud big data and machine learning products and services that support the data-to-AI lifecycle. It explores the processes, challenges, and benefits of building a big data pipeline and machine learning models with Vertex AI on Google Cloud.
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This course 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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This accelerated on-demand course introduces participants to the comprehensive and flexible infrastructure and platform services provided by Google Cloud. Through a combination of video lectures, demos, and hands-on labs, participants explore and deploy solution elements, including securely interconnecting networks, load balancing, autoscaling, infrastructure automation and managed services.
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This accelerated on-demand course introduces participants to the comprehensive and flexible infrastructure and platform services provided by Google Cloud with a focus on Compute Engine. Through a combination of video lectures, demos, and hands-on labs, participants explore and deploy solution elements, including infrastructure components such as networks, systems and applications services. This course also covers deploying practical solutions including customer-supplied encryption keys, security and access management, quotas and billing, and resource monitoring.
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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. Ein Skill-Logo ist ein exklusives digitales Abzeichen, das von Google Cloud ausgestellt wird und Ihre Kenntnisse über unsere Produkte und Dienste belegt. In diesem Zusammenhang wird auch die Fähigkeit bewertet, Ihr Wissen in einer interaktiven praxisnahen Umgebung anzuwenden. Absolvieren Sie eine kursspezifische Aufgabenreihe und die Challenge-Lab-Prüfung, um ein Skill-Logo zu erhalten, das Sie in Ihrem Netzwerk posten können.
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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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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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This course teaches participants techniques for monitoring and improving infrastructure and application performance in Google Cloud.
Using a combination of presentations, demos, hands-on labs, and real-world case studies, attendees gain experience with full-stack monitoring, real-time log management and analysis, debugging code in production, tracing application performance bottlenecks, and profiling CPU and memory usage.
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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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This course helps learners create a study plan for the PCA (Professional Cloud Architect) 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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This accelerated on-demand course introduces participants to the comprehensive and flexible infrastructure and platform services provided by Google Cloud with a focus on Compute Engine. Through a combination of video lectures, demos, and hands-on labs, participants explore and deploy solution elements, including infrastructure components such as networks, virtual machines and applications services. You will learn how to use the Google Cloud through the console and Cloud Shell. You'll also learn about the role of a cloud architect, approaches to infrastructure design, and virtual networking configuration with Virtual Private Cloud (VPC), Projects, Networks, Subnetworks, IP addresses, Routes, and Firewall rules.
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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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Welcome to the Getting Started with Google Kubernetes Engine course. If you're interested in Kubernetes, a software layer that sits between your applications and your hardware infrastructure, then you’re in the right place! Google Kubernetes Engine brings you Kubernetes as a managed service on Google Cloud.
The goal of this course is to introduce the basics of Google Kubernetes Engine, or GKE, as it’s commonly referred to, and how to get applications containerized and running in Google Cloud. The course starts with a basic introduction to Google Cloud, and is then followed by an overview of containers and Kubernetes, Kubernetes architecture, and Kubernetes operations.
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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.
AI Infrastructure: Cloud GPUs
Earned Okt 9, 2025 EDT
AI Infrastructure: Introduction to AI Hypercomputer
Earned Okt 9, 2025 EDT
Extend Gemini Enterprise Assistant Capabilities
Earned Okt 8, 2025 EDT
Create media search and media recommendations applications with AI Applications
Earned Okt 8, 2025 EDT
Configure AI Applications to optimize search results
Earned Okt 8, 2025 EDT
Create and maintain Vertex AI Search data stores
Earned Okt 7, 2025 EDT
Create Data Stores for Gen AI Applications
Earned Okt 7, 2025 EDT
Recommendations with AI Applications
Earned Okt 6, 2025 EDT
Introduction to NotebookLM
Earned Okt 6, 2025 EDT
Improve Vertex AI Search and Gemini Enterprise Search Results
Earned Okt 6, 2025 EDT
Vertex AI Search and Gemini Enterprise Analytics
Earned Okt 5, 2025 EDT
Vertex AI Search and Gemini Enterprise UI Configurations
Earned Okt 5, 2025 EDT
Build search and recommendations applications with AI Applications
Earned Okt 5, 2025 EDT
Introduction to AI Applications
Earned Okt 5, 2025 EDT
Getting Started with Terraform for Google Cloud
Earned Jul 21, 2025 EDT
Wissensaustausch mit Agentspace beschleunigen
Earned Mär 5, 2025 EST
Gemini für Cloud Architects
Earned Feb 8, 2025 EST
Gemini für DevOps Engineers
Earned Feb 8, 2025 EST
Introduction to Security in the World of AI
Earned Feb 7, 2025 EST
GCC Tech Learning Packs - TPU/GPU - SME Academy
Earned Jan 29, 2025 EST
Verantwortungsbewusste Anwendung von KI für Entwickler: Datenschutz und Sicherheit
Earned Jan 27, 2025 EST
Verantwortungsbewusste Anwendung von KI für Entwickler: Interpretierbarkeit und Transparenz
Earned Jan 27, 2025 EST
Verantwortungsbewusste Anwendung von KI für Entwickler: Fairness und Bias
Earned Jan 27, 2025 EST
Auf generativer KI basierende Anwendungen in Google Cloud entwickeln
Earned Jan 27, 2025 EST
ML Pipelines on Google Cloud
Earned Jan 25, 2025 EST
Machine Learning Operations (MLOps) mit Vertex AI: Modellbewertung
Earned Jan 25, 2025 EST
Machine Learning Operations (MLOps) für generative KI
Earned Jan 24, 2025 EST
Production Machine Learning Systems
Earned Jan 24, 2025 EST
Feature Engineering
Earned Jan 23, 2025 EST
Verantwortungsbewusste Anwendung von KI: KI-Grundsätze in Google Cloud anwenden
Earned Jan 21, 2025 EST
Working with Notebooks in Vertex AI
Earned Jan 16, 2025 EST
Einführung in KI und maschinelles Lernen in Google Cloud
Earned Jan 15, 2025 EST
Professional Machine Learning Engineer Study Guide
Earned Jan 14, 2025 EST
Umgebung für die Anwendungsentwicklung in Google Cloud einrichten
Earned Jun 3, 2024 EDT
Preparing for your Professional Cloud Architect Journey
Earned Jun 2, 2024 EDT
Einführung in die verantwortungsbewusste Anwendung von KI
Earned Mai 30, 2024 EDT
DEPRECATED Deploy Google Cloud Framework Data Foundation for SAP
Earned Jun 14, 2023 EDT
Transformer-Modelle und BERT-Modell
Earned Mai 13, 2023 EDT
Aufmerksamkeitsmechanismus
Earned Mai 12, 2023 EDT
Einführung in Large Language Models
Earned Mai 12, 2023 EDT
Einführung in generative KI
Earned Mai 12, 2023 EDT
How Google Does Machine Learning
Earned Apr 20, 2023 EDT
Create and Manage AlloyDB Instances
Earned Apr 12, 2023 EDT
Architecting with Google Kubernetes Engine: Foundations
Earned Feb 21, 2023 EST
Smart Analytics, Machine Learning, and AI on Google Cloud
Earned Jan 29, 2023 EST
Build Streaming Data Pipelines on Google Cloud
Earned Jan 29, 2023 EST
Build Batch Data Pipelines on Google Cloud
Earned Jan 29, 2023 EST
Build Data Lakes and Data Warehouses on Google Cloud
Earned Jan 27, 2023 EST
Preparing for Your Professional Cloud Security Engineer Journey
Earned Jan 8, 2023 EST
Serverless Data Processing with Dataflow: Foundations
Earned Jul 22, 2022 EDT
Google Cloud Big Data and Machine Learning Fundamentals
Earned Jul 10, 2022 EDT
Preparing for your Professional Data Engineer Journey
Earned Jul 9, 2022 EDT
Elastic Google Cloud Infrastructure: Scaling and Automation
Earned Jun 17, 2022 EDT
Essential Google Cloud Infrastructure: Core Services
Earned Jun 17, 2022 EDT
Kostenoptimierung für die Google Kubernetes Engine
Earned Jun 13, 2022 EDT
Google Cloud-Netzwerk einrichten
Earned Jun 12, 2022 EDT
Google Cloud-Netzwerk entwickeln
Earned Jun 12, 2022 EDT
Logging and Monitoring in Google Cloud
Earned Jun 10, 2022 EDT
Reliable Google Cloud Infrastructure: Design and Process
Earned Jun 9, 2022 EDT
Preparing for your Professional Cloud Architect Journey
Earned Jun 9, 2022 EDT
Essential Google Cloud Infrastructure: Foundation
Earned Jun 8, 2022 EDT
Google Cloud-Grundlagen: Kerninfrastruktur
Earned Jun 8, 2022 EDT
Getting Started with Google Kubernetes Engine
Earned Jun 8, 2022 EDT
Umgebung für die Anwendungsentwicklung in Google Cloud einrichten
Earned Mai 29, 2022 EDT