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Anuj Jaiswal

Mitglied seit 2020

Gold League

40110 Punkte
Den Wissensaustausch mit Gemini Enterprise beschleunigen Earned Apr 14, 2025 EDT
Build generative virtual agents with API integrations Earned Mär 17, 2025 EDT
Generative Playbooks Earned Mär 10, 2025 EDT
Building Complex Self-Service Experiences in Conversational Agents Earned Mär 10, 2025 EDT
Building Complex Self-Service Experiences in Conversational Agents Earned Mär 9, 2025 EDT
Advanced Performance Measurement Earned Mär 5, 2025 EST
Stateful Flows Earned Mär 5, 2025 EST
Incorporate Generative Features into Conversational Agent Flows Earned Mär 3, 2025 EST
Advanced Webhook Concepts Earned Feb 25, 2025 EST
Agent Summarization (Custom) Earned Feb 23, 2025 EST
Conversational AI Voice and Chat Integrations Earned Feb 19, 2025 EST
Leverage data with Customer Experience Insights Earned Feb 19, 2025 EST
Integrate Agent Assist with Telephony and Chatbot Systems Earned Feb 19, 2025 EST
Introduction to Agent Assist and its GenAI Capabilities Earned Feb 19, 2025 EST
Conversational Agents Quality Assurance and Deployment Lifecycle Earned Feb 18, 2025 EST
Build deterministic Virtual Agent enhanced with data stores Earned Jan 29, 2025 EST
Performance Measurement Earned Jan 29, 2025 EST
Webhook fundamentals Earned Jan 29, 2025 EST
Conversation Design Fundamentals Earned Jan 22, 2025 EST
DFCX Virtual Agent Delivery Framework Earned Jan 22, 2025 EST
Extend CX Agents with Vertex AI Search data stores Earned Okt 25, 2024 EDT
Customer Experience with Google AI Architecture Earned Okt 15, 2024 EDT
Intro to Conversational AI and Conversational AI Engagement Framework Earned Sep 25, 2024 EDT
Integrate Vertex AI Search and Conversation into Voice and Chat Apps Earned Mär 5, 2024 EST
Verantwortungsbewusste Anwendung von KI: KI-Grundsätze in Google Cloud anwenden Earned Jan 12, 2024 EST
Conversational AI on Vertex AI and Dialogflow CX Earned Jan 12, 2024 EST
Modelle zur Bilduntertitelung erstellen Earned Jun 14, 2023 EDT
Transformer-Modelle und BERT-Modell Earned Jun 14, 2023 EDT
Encoder-Decoder-Architektur Earned Jun 12, 2023 EDT
Aufmerksamkeitsmechanismus Earned Jun 9, 2023 EDT
Einstieg in die Bildgenerierung Earned Jun 9, 2023 EDT
Generative AI Fundamentals Earned Jun 9, 2023 EDT
Einführung in die verantwortungsbewusste Anwendung von KI Earned Jun 9, 2023 EDT
Einführung in Large Language Models Earned Jun 8, 2023 EDT
Einführung in generative KI Earned Jun 5, 2023 EDT
Contact Center as a Service Implementation Earned Mai 30, 2023 EDT
Virtual Agent Development in Dialogflow CX for Software Devs Earned Sep 7, 2022 EDT
Customer Experiences with Contact Center AI Earned Jun 28, 2021 EDT
Cloud Load Balancing in der Compute Engine implementieren Earned Sep 3, 2020 EDT

Gemini Enterprise ist ein leistungsstarkes Tool, das das Fachwissen von Google in den Bereichen Suche und KI zusammenbringt. Mitarbeitende können damit bestimmte Informationen in Dokumentenspeichern, E‑Mails, Chats, Ticketsystemen und anderen Datenquellen über eine einzige Suchleiste finden. Der Gemini Enterprise-Assistent kann sie auch beim Brainstorming, der Recherche oder der Strukturierung von Dokumenten unterstützen und zum Beispiel Kollegen zu einem Kalendertermin einladen, um die Wissensarbeit und Zusammenarbeit zu beschleunigen. (Gemini Enterprise hieß früher Google Agentspace. In diesem Kurs kann es daher noch Verweise auf den alten Produktnamen geben.)

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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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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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This course will equip you with the tools to develop complex conversational experiences in Conversational Agents capable of identifying the user intent and routing it to the right self service flow. Please note Dialogflow CX was recently renamed to Conversational Agents, Virtual agent renamed to Conversational agent, and CCAI Insights were renamed to Conversational Insights, and this course is in the process of being updated to reflect the new product names for Dialogflow CX, and Virtual Agent, CCAI Insights.

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This course will equip you with the tools to develop complex conversational experiences in Conversational Agents capable of identifying the user intent and routing it to the right self service flow.

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In this course, you will learn about advanced methods and tools to monitor the performance of your Conversational agent in Conversational Agents. 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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Discover flows in Conversational Agents and learn how to build deterministic chat and voice experiences with language models. Explore key concepts like drivers, intents, and entities, and how to use them to create conversational agents.

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Explore the Generative AI features for Conversational Agents and how to incorporate them into stateful Flows. Discover the possibilities with Generators, Generative Fallback, and Data Stores, as well as best practices and security settings for using these features.

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This course explores advanced technical considerations to optimize Webhook connectivity for comprehensive, end-to-end, Conversational Agent self-service experiences. Please note Dialogflow CX was recently renamed to Conversational Agents, Virtual agent renamed to Conversational agent, and CCAI Insights were renamed to Conversational Insights, and this course is in the process of being updated to reflect the new product names for Dialogflow CX, and Virtual Agent, CCAI Insights.

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In this course you will learn how Conversational AI Agent Assist can help distill complex customer interactions into concise and clear summaries. Please note Dialogflow CX was recently renamed to Conversational Agents, Virtual agent renamed to Conversational agent, and CCAI Insights were renamed to Conversational Insights, and this course is in the process of being updated to reflect the new product names for Dialogflow CX, and Virtual Agent, CCAI Insights.

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Learn about building conversational AI voice and chat integrations, including how telephony systems can connect with Google to enable phone-based interactions within the Conversational AI ecosystem. Explore key topics such as the differences between chat and voice conversations, the writing process for creating conversation scripts, and the beginning of the interrogative series and closing sequence.

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In this course you will learn how to leverage Customer Experience Insights (CX Insights) to uncover hidden information from your contact center data to increase operational efficiency and drive data-driven business decisions.

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In this course you will learn how Agent Assist can enhance the productivity of human agents while interacting with customers through the voice channel, as well as the options available for integration with other platforms in the Conversational AI ecosystem.

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This course will focus on Agent Assist, an AI-powered tool designed to enhance customer service interactions. In this course, you will learn how Agent Assist can enhance the productivity of human agents while interacting with customers through the chat channel. You’ll learn how to take full advantage of Agent Assist from Gemini Enterprise for Customer Experience, and its range of Gen AI features and functionality.

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This course explores the quality assurance best practices and the tools available in Conversational Agents to ensure production grade quality during Conversational Agent development, as well as the key tenets for the creation of a robust end to end deployment lifecycle. Please note Dialogflow CX was recently renamed to Conversational Agents, Virtual agent renamed to Conversational agent, and CCAI Insights were renamed to Conversational Insights, and this course is in the process of being updated to reflect the new product names for Dialogflow CX, and Virtual Agent, CCAI Insights.

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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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This course explores the fundamentals of the feedback loop process for Conversational Agent development and introduces the native capabilities within Conversational Agents that support it. You will also learn about advanced methods and tools to monitor the performance of your Conversational agent in Conversational Agents.

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In this course, you will learn the important role that different types of webhooks play in Conversational Agents development, and how to effectively integrate them into your routine configuration of a Conversational Agent. Please note Dialogflow CX was recently renamed to Conversational Agents, Virtual agent renamed to Conversational agent, and CCAI Insights were renamed to Conversational Insights, and this course is in the process of being updated to reflect the new product names for Dialogflow CX, and Virtual Agent, CCAI Insights.

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This course explores the foundational principles of conversation design to craft engaging and effective experiences that emulate human-like experiences specific to the Chat channel. Please note Dialogflow CX was recently renamed to Conversational Agents, Virtual agent renamed to Conversational agent, and CCAI Insights were renamed to Conversational Insights, and this course is in the process of being updated to reflect the new product names for Dialogflow CX, and Virtual Agent, CCAI Insights.

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This course explores the best practices, methods and tools to programmatically lead CCAI virtual agent delivery. It includes a high level overview of the end to end journey for building and deploying a virtual agent, as well as the core tenets to create a strong delivery culture. Additionally, this course covers the best practices for workflow management, defect tracking, release management and post-release support to ensure optimal virtual agent performance.

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In this course, you'll learn to develop AI agents that answer questions using websites, documents, or structured data. You will explore 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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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.

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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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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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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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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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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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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 Anwendungen basierend auf generativer KI entwickeln können.

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An AI-driven Contact Center as a Service (CCaaS) solution that is built natively on Google Cloud. The Implementation course provides Partners with essential training about the delivery of key features and functionality. The course explores how to leverage your key understanding of the product into successful customer implementation engagements with tips, best practices, guides, and more. Note: This product was previously called Contact Center AI (CCAI) Platform you may see references to that name still in the course, however the course is technically correct.

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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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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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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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