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kishore kalidindi

Учасник із 2017

Бронзова ліга

Кількість балів: 28000
Responsible AI: Applying AI Principles with Google Cloud - Yкраїнська Earned лют. 17, 2025 EST
[DEPRECATED] Advanced Performance Measurement Earned січ. 14, 2025 EST
Advanced Conversation Design Earned січ. 13, 2025 EST
Create Conversational Agents with Stateful Flows Earned січ. 12, 2025 EST
Conversation Design Fundamentals Earned січ. 9, 2025 EST
[DEPRECATED] DFCX Bot Building Quality Assurance and Deployment Lifecycle Earned січ. 3, 2025 EST
[Deprecated] Virtual FAQ with data store agents Earned січ. 2, 2025 EST
Conversational AI Voice and Chat Integrations Earned груд. 16, 2024 EST
[DEPRECATED] Basic Performance Measurement Earned груд. 16, 2024 EST
[DEPRECATED] Agent Summarization (Custom) Earned груд. 16, 2024 EST
Conversational AI and its Engagement Framework Earned груд. 15, 2024 EST
Agent Assist and its Gen AI Capabilities Earned груд. 13, 2024 EST
Implement Enterprise Design Patterns with CX Agent Studio Earned груд. 13, 2024 EST
[Deprecated] Advanced Webhook Concepts Earned груд. 12, 2024 EST
[DEPRECATED] Webhook fundamentals Earned груд. 12, 2024 EST
Machine Learning Operations (MLOps) for Generative AI Earned лют. 20, 2024 EST
Machine Learning Operations (MLOps): Getting Started Earned лип. 21, 2023 EDT
Introduction to Vertex AI Studio Earned черв. 25, 2023 EDT
Create Image Captioning Models Earned черв. 25, 2023 EDT
Transformer Models and BERT Model Earned черв. 17, 2023 EDT
Attention Mechanism Earned черв. 16, 2023 EDT
Encoder-Decoder Architecture Earned черв. 16, 2023 EDT
Introduction to Image Generation Earned черв. 15, 2023 EDT
Generative AI Fundamentals - Українська Earned черв. 15, 2023 EDT
Introduction to Responsible AI - Українська Earned черв. 15, 2023 EDT
Introduction to Large Language Models - Українська Earned черв. 15, 2023 EDT
Introduction to Generative AI - Українська Earned черв. 14, 2023 EDT
Kubernetes in Google Cloud Earned серп. 5, 2019 EDT
Automate Deployment and Manage Traffic on a Google Cloud Network Earned трав. 6, 2019 EDT

Що більше штучний інтелект і машинне навчання використовуються в корпоративних середовищах, то нагальнішою стає потреба розробити принципи відповідального ставлення до них. Однак говорити про принципи відповідального використання штучного інтелекту легше, ніж застосовувати їх на практиці. Цей курс допоможе вам дізнатись, як запровадити відповідальну роботу зі штучним інтелектом у вашій організації. У цьому курсі ви дізнаєтеся про підхід Google Cloud до відповідального використання ШІ, а також отримаєте практичні поради й набудете досвіду, який допоможе вам розробити власний підхід до цього завдання.

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In this course, you will learn about advanced methods and tools to monitor the performance of your virtual agents in Dialogflow CX

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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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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. Explore other content in the Gemini Agent Ready (GEAR) program.

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This course explores the foundational principles of conversation design to craft engaging and effective chatbot experiences that emulate human-like experiences. Explore other content in the Gemini Agent Ready (GEAR) program.

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This course explores the quality assurance best practices and the tools available in Dialogflow CX to ensure production grade quality during Virtual Agent development, as well as the key tenets for the creation of a robust end to end deployment lifecycle

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In this course, you will learn how to develop a generative agent capable of answering questions from websites, documents, and/or unstructured data.

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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 the fundamentals of the feedback loop process for Virtual Agent development and introduces the native capabilities within Dialogflow CX that support it

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In this course you will learn how Contact Center AI Agent Assist can help distill complex customer interactions into concise and clear summaries.

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Transform your understanding of customer service with this course on Gemini Enterprise for Customer Experience (GECX) and its powerful generative AI capabilities. You'll start by tracing the journey of contact centers, understanding how they've evolved and where gen AI is propelling them next. Then, you'll gain a deep understanding of the core building blocks within the GECX solution, seeing how each component contributes to delivering exceptional customer experiences. The course concludes by exploring the robust business case for GECX, along with practical use cases and the various user personas that benefit from this innovative solution. Explore other content in the Gemini Agent Ready (GEAR) program.

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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. Explore other content in the Gemini Agent Ready (GEAR) program.

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This course explores using enterprise design patterns to build scalable, reliable, and secure agents within CX Agent Studio. Additionally, it covers navigating the complete development lifecycle by deploying multi-layered application security and locking down cloud infrastructure perimeters. It details the core pillars of multi-turn session persistence and real-time audio supervision to facilitate safe, continuous conversational experiences.

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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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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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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 Gemini Enterprise Agent Platform empowers AI teams to streamline MLOps processes and achieve success in Generative AI projects.

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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 introduces Vertex AI Studio, a tool to interact with generative AI models, prototype business ideas, and launch them into production. Through an immersive use case, engaging lessons, and a hands-on lab, you’ll explore the prompt-to-product lifecycle and learn how to leverage Vertex AI Studio for Gemini multimodal applications, prompt design, prompt engineering, and model tuning. The aim is to enable you to unlock the potential of gen AI in your projects with Vertex AI Studio.

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This course teaches you how to create an image captioning model by using deep learning. You learn about the different components of an image captioning model, such as the encoder and decoder, and how to train and evaluate your model. By the end of this course, you will be able to create your own image captioning models and use them to generate captions for images

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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 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 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 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 Gemini Enterprise Agent Platform.

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Щоб отримати кваліфікаційний значок, пройдіть курси "Introduction to Generative AI", "Introduction to Large Language Models" й "Introduction to Responsible AI". Пройшовши завершальний тест, ви підтвердите, що засвоїли основні поняття, які стосуються генеративного штучного інтелекту. Кваліфікаційний значок – це цифровий значок від платформи Google Cloud, який свідчить, що ви знаєтеся на продуктах і сервісах Google Cloud. Щоб опублікувати кваліфікаційний значок, зробіть свій профіль загальнодоступним, а також додайте значок у профіль у соціальних мережах.

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Це ознайомлювальний курс мікронавчання, який має пояснити, що таке відповідальне використання штучного інтелекту, чому воно важливе і як компанія Google реалізує його у своїх продуктах. Крім того, у цьому курсі викладено 7 принципів Google щодо штучного інтелекту.

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У цьому ознайомлювальному курсі мікронавчання ви дізнаєтеся, що таке великі мовні моделі, де вони використовуються і як підвищити їх ефективність коригуванням запитів. Він також охоплює інструменти Google, які допоможуть вам створювати власні додатки на основі генеративного штучного інтелекту.

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Це ознайомлювальний курс мікронавчання, який має пояснити, що таке генеративний штучний інтелект, як він використовується й чим відрізняється від традиційних методів машинного навчання. Він також охоплює інструменти Google, які допоможуть вам створювати власні додатки на основі генеративного штучногоінтелекту.

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Kubernetes is the most popular container orchestration system, and Google Kubernetes Engine was designed specifically to support managed Kubernetes deployments in Google Cloud. In this course, you will get hands-on practice configuring Docker images, containers, and deploying fully-fledged Kubernetes Engine applications.

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Networking is a principle theme of cloud computing. It’s the underlying structure of Google Cloud, and it’s what connects all your resources and services to one another. This course will cover essential Google Cloud networking services and will give you hands-on practice with specialized tools for developing mature networks. From learning the ins-and-outs of VPCs, to creating enterprise-grade load balancers, Automate Deployment and Manage Traffic on a Google Cloud Network will give you the practical experience needed so you can start building robust networks right away.

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