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Vinod Konkal

Member since 2024

Silver League

22880 points
Create Agents with Generative Playbooks Earned Feb 10, 2025 EST
Building a Virtual Agent with Dialogflow CX Earned Nov 21, 2024 EST
Agent Assist Voice and Integrations Earned Nov 20, 2024 EST
Agent Assist and its Gen AI Capabilities Earned Nov 19, 2024 EST
Advanced Conversation Design Earned Nov 18, 2024 EST
Conversational AI Voice and Chat Integrations Earned Nov 4, 2024 EST
Advanced Webhook Concepts Earned Nov 4, 2024 EST
Building Complex End to End Self-Service Experiences in Dialogflow CX Earned Oct 31, 2024 EDT
Transformer Models and BERT Model Earned Oct 28, 2024 EDT
Encoder-Decoder Architecture Earned Oct 28, 2024 EDT
Attention Mechanism Earned Oct 26, 2024 EDT
Introduction to Image Generation Earned Oct 26, 2024 EDT
Machine Learning Operations (MLOps) for Generative AI Earned Oct 25, 2024 EDT
Responsible AI: Applying AI Principles with Google Cloud Earned Oct 25, 2024 EDT
Prompt Design in Vertex AI Earned Oct 21, 2024 EDT
Incorporating Generative Features into Complex DFCX Agents Earned Oct 20, 2024 EDT
Webhook fundamentals Earned Oct 17, 2024 EDT
Conversational Insights Earned Oct 17, 2024 EDT
Introduction to Responsible AI Earned Jul 15, 2024 EDT
Introduction to Large Language Models Earned Jul 15, 2024 EDT
Introduction to Generative AI Earned Jul 15, 2024 EDT

This course will teach you how to build conversational experiences for Conversational Agents using Generative Playbooks. You'll start with an introduction to playbooks and learn how to set up your first one. You'll also learn about the importance of testing, as well as key production considerations like quota limits and integration. The course concludes with a case study that shows how to use playbooks for generative steering.

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Learn how to build a basic virtual agent for your contact center using Dialogflow CX.

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In this course you will learn how Contact Center AI 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 CCAI ecosystem.

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Unlock the power of generative AI to create intelligent, automated agents. After completing this course, you'll be equipped to develop a data store agent that can instantly answer complex questions by automatically extracting and synthesizing information from your websites, documents, or structured data. Say goodbye to static FAQs—your new agent will provide dynamic, accurate answers and even surface the original source URLs, all with a simple and rapid setup.

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In this course, you will learn the advanced conversational design principles for both the Voice and Caht channels to craft engaging and effective end-to-end experiences that emulate human-like interactions.

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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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This course explores advanced technical considerations to optimize Webhook connectivity for comprehensive, end-to-end, Virtual Agent self-service experiences.

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

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This course introduces you to the Transformer architecture and the Bidirectional Encoder Representations from Transformers (BERT) model. You learn about the main components of the Transformer architecture, such as the self-attention mechanism, and how it is used to build the BERT model. You also learn about the different tasks that BERT can be used for, such as text classification, question answering, and natural language inference.This course is estimated to take approximately 45 minutes to complete.

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This course gives you a synopsis of the encoder-decoder architecture, which is a powerful and prevalent machine learning architecture for sequence-to-sequence tasks such as machine translation, text summarization, and question answering. You learn about the main components of the encoder-decoder architecture and how to train and serve these models. In the corresponding lab walkthrough, you’ll code in TensorFlow a simple implementation of the encoder-decoder architecture for poetry generation from the beginning.

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This course will introduce you to the attention mechanism, a powerful technique that allows neural networks to focus on specific parts of an input sequence. You will learn how attention works, and how it can be used to improve the performance of a variety of machine learning tasks, including machine translation, text summarization, and question answering. This course is estimated to take approximately 45 minutes to complete.

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This course introduces diffusion models, a family of machine learning models that recently showed promise in the image generation space. Diffusion models draw inspiration from physics, specifically thermodynamics. Within the last few years, diffusion models became popular in both research and industry. Diffusion models underpin many state-of-the-art image generation models and tools on Google Cloud. This course introduces you to the theory behind diffusion models and how to train and deploy them on Vertex AI.

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This course is dedicated to equipping you with the knowledge and tools needed to uncover the unique challenges faced by MLOps teams when deploying and managing Generative AI models, and exploring how Vertex AI empowers AI teams to streamline MLOps processes and achieve success in Generative AI projects.

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As the use of enterprise Artificial Intelligence and Machine Learning continues to grow, so too does the importance of building it responsibly. A challenge for many is that talking about responsible AI can be easier than putting it into practice. If you’re interested in learning how to operationalize responsible AI in your organization, this course is for you. In this course, you will learn how Google Cloud does this today, together with best practices and lessons learned, to serve as a framework for you to build your own responsible AI approach.

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Complete the introductory Prompt Design in Vertex AI skill badge to demonstrate skills in the following: prompt engineering, image analysis, and multimodal generative techniques, within Vertex AI. Discover how to craft effective prompts, guide generative AI output, and apply Gemini models to real-world marketing scenarios.

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In this course you will learn how to integrate multiple advanced generative capabilities within a Dialogflow CX agent.

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In this course, you will learn the important role that different types of webhooks play in Dialogflow CX development, and how to effectively integrate them into your routine configuration of a Virtual Agent.

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

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This is an introductory-level microlearning course aimed at explaining what responsible AI is, why it's important, and how Google implements responsible AI in their products. It also introduces Google's 3 AI principles.

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This is an introductory level micro-learning course that explores what large language models (LLM) are, the use cases where they can be utilized, and how you can use prompt tuning to enhance LLM performance. It also covers Google tools to help you develop your own Gen AI apps.

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This is an introductory level microlearning course aimed at explaining what Generative AI is, how it is used, and how it differs from traditional machine learning methods. It also covers Google Tools to help you develop your own Gen AI apps.

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