Evaluate Gen AI model and agent performance
Earned Aug 27, 2025 EDT
Model evaluation on Vertex AI
Earned Aug 26, 2025 EDT
Evaluate ADK Agents with Vertex AI Gen AI Evaluation Service
Earned Aug 21, 2025 EDT
Deploy a RAG application with vector search in Firestore
Earned Aug 19, 2025 EDT
Implement RAG with Vertex AI
Earned Aug 17, 2025 EDT
Find, Explore and Deploy Model Garden Models
Earned Aug 17, 2025 EDT
Improve Performance by Fine-Tuning Foundation Models
Earned Aug 16, 2025 EDT
Build Gen AI solutions using Model Garden models and APIs
Earned Aug 15, 2025 EDT
Edit images with Imagen
Earned Aug 13, 2025 EDT
Generate and Edit Media with Imagen, Gemini, and Veo
Earned Aug 12, 2025 EDT
Implement Hybrid Search
Earned Aug 12, 2025 EDT
Einbettungen, Vektorsuche und RAG mit BigQuery erstellen
Earned Aug 12, 2025 EDT
Explore Google's Gen AI Models
Earned Aug 12, 2025 EDT
Engineer Effective Prompts for Generative Models
Earned Aug 6, 2025 EDT
Empower Gen AI apps with tool use
Earned Aug 6, 2025 EDT
Extend Gemini with controlled generation and Tool use
Earned Jul 25, 2025 EDT
Virtual FAQ with data store agents
Earned Jun 17, 2025 EDT
Build basic Conversational Agents with Playbooks and Flows
Earned Jun 16, 2025 EDT
Generative Playbooks
Earned Jun 16, 2025 EDT
Build deterministic Virtual Agent enhanced with data stores
Earned Jan 17, 2025 EST
Build generative virtual agents with API integrations
Earned Jan 17, 2025 EST
Customer Engagement Suite with Google AI Architecture
Earned Okt 21, 2024 EDT
Intro to Conversational AI and Conversational AI Engagement Framework
Earned Okt 21, 2024 EDT
Build and Deploy a Generative AI solution using a RAG framework
Earned Aug 3, 2024 EDT
Machine Learning Operations (MLOps) with Vertex AI: Manage Features
Earned Jun 7, 2024 EDT
Machine Learning Operations (MLOps): Getting Started
Earned Jun 6, 2024 EDT
Introduction to CES and Conversational Agents
Earned Jun 6, 2024 EDT
Feature Engineering
Earned Jun 3, 2024 EDT
Build, Train and Deploy ML Models with Keras on Google Cloud
Earned Mai 29, 2024 EDT
Launching into Machine Learning
Earned Mai 28, 2024 EDT
Einführung in Vertex AI Studio
Earned Mai 24, 2024 EDT
Generative AI for Business Leaders
Earned Mai 24, 2024 EDT
Einführung in KI und maschinelles Lernen in Google Cloud
Earned Mai 24, 2024 EDT
Develop Advanced Enterprise Search and Conversation Applications
Earned Mai 8, 2024 EDT
Custom Search with Embeddings in Vertex AI
Earned Mai 8, 2024 EDT
Vektorsuche und Einbettungen
Earned Mai 7, 2024 EDT
Develop Advanced Enterprise Search and Conversation Applications
Earned Feb 1, 2024 EST
Customer Experiences with Contact Center AI
Earned Jan 30, 2024 EST
Text Prompt Engineering Techniques
Earned Jan 25, 2024 EST
Generative AI Explorer : Vertex AI
Earned Jan 24, 2024 EST
Einführung in Vertex AI Studio
Earned Jan 19, 2024 EST
Einstieg in die Bildgenerierung
Earned Jan 19, 2024 EST
Modelle zur Bilduntertitelung erstellen
Earned Jan 18, 2024 EST
Encoder-Decoder-Architektur
Earned Jan 18, 2024 EST
Transformer-Modelle und BERT-Modell
Earned Jan 18, 2024 EST
Aufmerksamkeitsmechanismus
Earned Jan 18, 2024 EST
Integrate Vertex AI Search and Conversation into Voice and Chat Apps
Earned Jan 11, 2024 EST
Conversational AI on Vertex AI and Dialogflow CX
Earned Jan 9, 2024 EST
Building Gen AI Apps with Vertex AI: Prompting and Tuning
Earned Jan 1, 2024 EST
Implementing Generative AI with Vertex AI
Earned Dez 9, 2023 EST
Verantwortungsbewusste Anwendung von KI: KI-Grundsätze in Google Cloud anwenden
Earned Dez 4, 2023 EST
Generative AI Fundamentals
Earned Dez 3, 2023 EST
Einführung in die verantwortungsbewusste Anwendung von KI
Earned Dez 3, 2023 EST
Einführung in Large Language Models
Earned Dez 3, 2023 EST
CCAI Operations and Implementation
Earned Jun 16, 2023 EDT
Virtual Agent Development in Dialogflow CX for Software Devs
Earned Jun 15, 2023 EDT
Virtual Agent Development in Dialogflow CX for Citizen Devs
Earned Jun 14, 2023 EDT
Einführung in generative KI
Earned Jun 12, 2023 EDT
Contact Center AI: Conversational Design Fundamentals
Earned Jun 12, 2023 EDT
DEPRECATED Create Conversational AI Agents with Dialogflow CX
Earned Jun 7, 2023 EDT
Complete the Evaluate Gen AI model and agent performance skill badge to demonstrate your ability to use the Gen AI evaluation service. You will evaluate models to select the best model for a given task, compare models against each other and evaluate the performance of agents.
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 delves into the complexities of assessing the quality of large language model outputs. It examines the challenges enterprises face due to the subjective and sometimes incorrect nature of LLM responses, including hallucinations and inconsistent results. The course introduces various evaluation metrics for different tasks like classification, text generation, and question answering, such as Accuracy, Precision, Recall, F1 score, ROUGE, BLEU, and Exact Match. It also explores evaluation methods offered by Vertex AI LLM Evaluation Services, including computation-based, autorater, and human evaluation, providing insights into their application and benefits. Finally, the module covers how to unit test LLM applications within Vertex AI.
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Evaluation is important at every step of your Gen AI development process. In this course you will learn how to evaluate gen AI agents built using agent frameworks.
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This lab tests your ability to develop a real-world Generative AI Q&A solution using a RAG framework. You will use Firestore as a vector database and deploy a Flask app as a user interface to query a food safety knowledge base.
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Learn how to build your own Retrieval-Augmented Generation (RAG) solutions for greater control and flexibility than out-of-the-box implementations. Create a custom RAG solution using Vertex AI APIs, vector stores, and the LangChain framework.
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Model Garden is a model library that helps you discover, test, and deploy models from Google and Google partners. Learn how to explore the available models and select the right ones for your use case. And how to deploy and interact with Model Garden models through the Google Cloud console and APIs.
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Model tuning is an effective way to customize large models to your tasks. It's a key step to improve the model's quality and efficiency. Model tuning provides benefits such as higher quality results for your specific tasks and increased model robustness. You learn some of the tuning options available in Vertex AI and when to use them.
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Complete the Develop solutions using Model Garden APIs skill badge to demonstrate your ability to use Vertex AI Model Garden features when building gen AI solutions. You will use partner APIs such as Anthropic Claude ands Meta Llama, deploy and programatically access foundation models like Gemma and Stable Diffusion XL and access Vertex AI Endpoints.
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 Edit images with Imagen skill badge to demonstrate your skills with Imagen's mask modes and editing modes to edit images according to certain prompts.
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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Generate engaging media with Google's foundation models for media. Create new images with Imagen, or edit your existing photos by adding details or outpainting to create a wider view. Replace backgrounds to put your products in new scenes. And learn the basics of generating videos with Veo!
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Learn how to create Hybrid Search applications using Vertex AI Vertex Search to combine semantic searching with keyword search to return results based on both semantic meaning and keyword matching.
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In diesem Kurs wird eine Lösung für Retrieval-Augmented Generation (RAG) in BigQuery vorgestellt, die KI-Halluzinationen minimiert. Sie lernen einen RAG-Workflow kennen, der die Erstellung von Einbettungen, die Suche in einem Vektorraum und die Generierung verbesserter Antworten umfasst. Darüber hinaus werden die konzeptionellen Gründe für diese Schritte und ihre praktische Umsetzung mit BigQuery erklärt. Am Ende des Kurses werden Sie in der Lage sein, eine RAG-Pipeline mithilfe von BigQuery und generativen KI-Modellen wie Gemini zu erstellen und Modelle einzubetten, um KI-Halluzinationen zu verhindern.
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Learn how to leverage Gemini multimodal capabilities to process and generate text, images, and audio and to integrate Gemini through APIs to perform tasks such as content creation and summarization.
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Learn a variety of strategies and techniques to engineer effective prompts for generative models
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An LLM-based application can process language in a way that resembles thought. But if you want to extend its capabilities to take actions by running other functions you have coded, you will need to use function calling. This can also be referred to as tool use. Additionally, you can give a model the ability to search Google or search a data store of documents to ground its responses. In other words, to base its answers on that information. In this course, you’ll explore these concepts.
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Complete the Extend Gemini with controlled generation and Tool use skill badge to demonstrate your proficiency in connecting models to external tools and APIs. This allows models to augment their knowledge, extend their capabilities and interact with external systems to take actions such as sending an email.
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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In this course, you'll learn to develop generative agents that answer questions using websites, documents, or structured data. You will explore Vertex AI Applications and understand the advantages of data store agents, including their scalability and security.
You'll learn about different data store types and also discover how to connect data stores to agents and add personalization for enhanced responses.
Finally, you'll gain insights into common search configurations and troubleshooting techniques.
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Complete the Build basic Conversational Agents with Playbooks and Flows skill badge to demonstrate your proficiency in building virtual agents using traditional NLU and generative-based 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 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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Explore Playbooks and their implementation of the ReAct pattern for building Conversational Agents. You will learn how to construct a Playbook, set up goals and instructions to build a chatbot in natural language, and learn to test and deploy your solution.
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Demonstrate the ability to create and deploy deterministic virtual agents using Dialgflow CX and augment responses by grounding results on your own data integrating with Vertex AI Agent Builder data stores and leveraging Gemini for summarizations.
You will use the following technologies and Google Cloud services:
Vertex AI Agent Builder
Dialogflow CX
Gemini
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Demonstrate the ability to create and deploy generative virtual agents with natural language using Vertex AI Agent Builder and augment responses by integrating Gemini responses with third party APIs and your own data stores
You will use the following technologies and Google Cloud services:
Vertex AI Agent Builder
Gemini
Cloud Functions
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In this course you will learn the key architectural considerations that need to be taken into account when designing for the implementation of Conversational AI solutions. Please note Dialogflow CX was recently renamed to Conversational Agents and CCAI Insights was renamed to Conversational Insights.
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This is an introductory course to all solutions in the Conversational AI portfolio and the Gen AI features that are available to transform them. The course also explores the business case around Conversational AI, and the use cases and user personas addressed by the solution. Please note Dialogflow CX was recently renamed to Conversational Agents and this course is in the process of being updated to reflect the new product name for Dialogflow CX.
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Demonstrate your ability to implement updated prompt engineering techniques and utilize several of Gemini's key capacilities including multimodal understanding and function calling. Then integrate generative AI into a RAG application deployed to Cloud Run. This course contains labs that are to be used as a test environment. They are deployed to test your understanding as a learner with a limited scope. These technologies can be used with fewer limitations in a real world environment.
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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.
Learners will get hands-on practice using Vertex AI Feature Store's streaming ingestion at the SDK layer.
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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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This course explores the different products and capabilities of Customer Engagement Suite (CES) and Conversational agents. Additionally, it covers the foundational principles of conversation design to craft engaging and effective experiences that emulate human-like experiences specific to the Chat channel.
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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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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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A Business Leader in Generative AI can articulate the capabilities of core cloud Generative AI products and services and understand how they benefit organizations. This course provides an overview of the types of opportunities and challenges that companies often encounter in their digital transformation journey and how they can leverage Google Cloud's generative AI products to overcome these challenges.
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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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In this course, you'll use text embeddings for tasks like classification, outlier detection, text clustering and semantic search. You'll combine semantic search with the text generation capabilities of an LLM to build Retrieval Augmented Generation (RAG) solutions, such as for question-answering systems, using Google Cloud's Vertex AI and Google Cloud databases.
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This course explores Google Cloud technologies to create and generate embeddings. Embeddings are numerical representations of text, images, video and audio, and play a pivotal role in many tasks that involve the identification of similar items, like Google searches, online shopping recommendations, and personalized music suggestions. Specifically, you’ll use embeddings for tasks like classification, outlier detection, clustering and semantic search.
You’ll combine semantic search with the text generation capabilities of an LLM to build Retrieval Augmented Generation (RAG) systems and question-answering solutions, on your own proprietary data using Google Cloud’s Vertex AI.
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In diesem Kurs lernen Sie KI-basierte Suchtechnologien, Tools und Anwendungen kennen. Er umfasst folgende Themen: die semantische Suche mithilfe von Vektoreinbettungen, die Hybridsuche, bei der semantische und stichwortbezogene Ansätze kombiniert werden, und Retrieval-Augmented Generation (RAG), die KI-Halluzinationen durch einen fundierten KI-Agenten minimiert. Sie sammeln praktische Erfahrungen mit der Vektorsuche in Vertex AI zum Entwickeln einer intelligenten Suchmaschine.
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In this course, you'll use text embeddings for tasks like classification, outlier detection, text clustering and semantic search. You'll combine semantic search with the text generation capabilities of an LLM to build Retrieval Augmented Generation (RAG) solutions, such as for question-answering systems, using Google Cloud's Vertex AI and Google Cloud databases.
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Learn how to design, develop, and deploy customer conversational solutions using Contact Center Artificial Intelligence (CCAI). You'll also 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.
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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 content is deprecated. Please see the latest version of the course, here.
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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 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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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 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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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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This course on Integrate Vertex AI Search and Conversation into Voice and Chat Apps is composed of a set of labs to give you a hands on experience to interacting with new Generative AI technologies. You will learn how to create end-to-end search and conversational experiences by following examples. These technologies complement predefined intent-based chat experiences created in Dialogflow with LLM-based, generative answers that can be based on your own data. Also, they allow you to porvide enterprise-grade search experiences for internal and external websites to search documents, structure data and public websites.
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In this course you will learn how to use the new generative AI features in Dialogflow CX to create virtual agents that can have more natural and engaging conversations with customers. Discover how to deploy generative fallback responses to gracefully handle errors and omissions in customer conversations, deploy generators to increase intent coverage, and structure, ingest, and manage data in a data store. And explore how to deploy and maintain generative AI agents using your data, and deploy and maintain hybrid agents in combination with existing intent-based design paradigms.
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(This course was previously named Multimodal Prompt Engineering with Gemini and PaLM) This course teaches how to use Vertex AI Studio, a Google Cloud console tool for rapidly prototyping and testing generative AI models. You learn to test sample prompts, design your own prompts, and customize foundation models to handle tasks that meet your application's needs. Whether you are looking for text, chat, code, image or speech generative experiences Vertex AI Studio offers you an interface to work with and APIs to integrate your production application.
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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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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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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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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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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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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.
Evaluate Gen AI model and agent performance
Earned Aug 27, 2025 EDT
Model evaluation on Vertex AI
Earned Aug 26, 2025 EDT
Evaluate ADK Agents with Vertex AI Gen AI Evaluation Service
Earned Aug 21, 2025 EDT
Deploy a RAG application with vector search in Firestore
Earned Aug 19, 2025 EDT
Implement RAG with Vertex AI
Earned Aug 17, 2025 EDT
Find, Explore and Deploy Model Garden Models
Earned Aug 17, 2025 EDT
Improve Performance by Fine-Tuning Foundation Models
Earned Aug 16, 2025 EDT
Build Gen AI solutions using Model Garden models and APIs
Earned Aug 15, 2025 EDT
Edit images with Imagen
Earned Aug 13, 2025 EDT
Generate and Edit Media with Imagen, Gemini, and Veo
Earned Aug 12, 2025 EDT
Implement Hybrid Search
Earned Aug 12, 2025 EDT
Einbettungen, Vektorsuche und RAG mit BigQuery erstellen
Earned Aug 12, 2025 EDT
Explore Google's Gen AI Models
Earned Aug 12, 2025 EDT
Engineer Effective Prompts for Generative Models
Earned Aug 6, 2025 EDT
Empower Gen AI apps with tool use
Earned Aug 6, 2025 EDT
Extend Gemini with controlled generation and Tool use
Earned Jul 25, 2025 EDT
Virtual FAQ with data store agents
Earned Jun 17, 2025 EDT
Build basic Conversational Agents with Playbooks and Flows
Earned Jun 16, 2025 EDT
Generative Playbooks
Earned Jun 16, 2025 EDT
Build deterministic Virtual Agent enhanced with data stores
Earned Jan 17, 2025 EST
Build generative virtual agents with API integrations
Earned Jan 17, 2025 EST
Customer Engagement Suite with Google AI Architecture
Earned Okt 21, 2024 EDT
Intro to Conversational AI and Conversational AI Engagement Framework
Earned Okt 21, 2024 EDT
Build and Deploy a Generative AI solution using a RAG framework
Earned Aug 3, 2024 EDT
Machine Learning Operations (MLOps) with Vertex AI: Manage Features
Earned Jun 7, 2024 EDT
Machine Learning Operations (MLOps): Getting Started
Earned Jun 6, 2024 EDT
Introduction to CES and Conversational Agents
Earned Jun 6, 2024 EDT
Feature Engineering
Earned Jun 3, 2024 EDT
Build, Train and Deploy ML Models with Keras on Google Cloud
Earned Mai 29, 2024 EDT
Launching into Machine Learning
Earned Mai 28, 2024 EDT
Einführung in Vertex AI Studio
Earned Mai 24, 2024 EDT
Generative AI for Business Leaders
Earned Mai 24, 2024 EDT
Einführung in KI und maschinelles Lernen in Google Cloud
Earned Mai 24, 2024 EDT
Develop Advanced Enterprise Search and Conversation Applications
Earned Mai 8, 2024 EDT
Custom Search with Embeddings in Vertex AI
Earned Mai 8, 2024 EDT
Vektorsuche und Einbettungen
Earned Mai 7, 2024 EDT
Develop Advanced Enterprise Search and Conversation Applications
Earned Feb 1, 2024 EST
Customer Experiences with Contact Center AI
Earned Jan 30, 2024 EST
Text Prompt Engineering Techniques
Earned Jan 25, 2024 EST
Generative AI Explorer : Vertex AI
Earned Jan 24, 2024 EST
Einführung in Vertex AI Studio
Earned Jan 19, 2024 EST
Einstieg in die Bildgenerierung
Earned Jan 19, 2024 EST
Modelle zur Bilduntertitelung erstellen
Earned Jan 18, 2024 EST
Encoder-Decoder-Architektur
Earned Jan 18, 2024 EST
Transformer-Modelle und BERT-Modell
Earned Jan 18, 2024 EST
Aufmerksamkeitsmechanismus
Earned Jan 18, 2024 EST
Integrate Vertex AI Search and Conversation into Voice and Chat Apps
Earned Jan 11, 2024 EST
Conversational AI on Vertex AI and Dialogflow CX
Earned Jan 9, 2024 EST
Building Gen AI Apps with Vertex AI: Prompting and Tuning
Earned Jan 1, 2024 EST
Implementing Generative AI with Vertex AI
Earned Dez 9, 2023 EST
Verantwortungsbewusste Anwendung von KI: KI-Grundsätze in Google Cloud anwenden
Earned Dez 4, 2023 EST
Generative AI Fundamentals
Earned Dez 3, 2023 EST
Einführung in die verantwortungsbewusste Anwendung von KI
Earned Dez 3, 2023 EST
Einführung in Large Language Models
Earned Dez 3, 2023 EST
CCAI Operations and Implementation
Earned Jun 16, 2023 EDT
Virtual Agent Development in Dialogflow CX for Software Devs
Earned Jun 15, 2023 EDT
Virtual Agent Development in Dialogflow CX for Citizen Devs
Earned Jun 14, 2023 EDT
Einführung in generative KI
Earned Jun 12, 2023 EDT
Contact Center AI: Conversational Design Fundamentals
Earned Jun 12, 2023 EDT
DEPRECATED Create Conversational AI Agents with Dialogflow CX
Earned Jun 7, 2023 EDT