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vinod soni

Jest członkiem od 2025

Liga diamentowa

14612 pkt.
Extend Gemini with controlled generation and Tool use Earned kwi 18, 2026 EDT
Engineer Data for Predictive Modeling with BigQuery ML Earned lut 20, 2026 EST
Improve customer and agent satisfaction with Agent Assist Earned lut 7, 2026 EST
Integrate Agent Assist with Telephony and Chatbot Systems Earned lut 7, 2026 EST
Introduction to Agent Assist and its GenAI Capabilities Earned lut 1, 2026 EST
Build search and recommendations applications with AI Applications Earned sty 19, 2026 EST
Responsible AI for Digital Leaders with Google Cloud Earned sty 13, 2026 EST
Empower Gen AI Apps with Tool Use Earned gru 31, 2025 EST
Supervised Fine-tuning for Gemini Earned gru 30, 2025 EST
Machine Learning Operations (MLOps) with Vertex AI: Model Evaluation Earned gru 30, 2025 EST
Model evaluation on Vertex AI Earned gru 30, 2025 EST
Extend CX Agents with Vertex AI Search data stores Earned gru 29, 2025 EST
Incorporate Generative Features into Conversational Agent Flows Earned gru 29, 2025 EST
Create Conversational Agents with Stateful Flows Earned gru 29, 2025 EST
Find, Explore and Deploy Model Garden Models Earned gru 29, 2025 EST
Deploy a RAG application with vector search in Firestore Earned gru 28, 2025 EST
Generative Playbooks Earned gru 26, 2025 EST
Introduction to Gemini Enterprise for Customer Experience Earned gru 26, 2025 EST
Customer Experience with Google AI Architecture Earned gru 26, 2025 EST
Text Prompt Engineering Techniques Earned gru 26, 2025 EST
Engineer Effective Prompts for Generative Models Earned gru 26, 2025 EST
Explore Google's Gen AI Models Earned gru 26, 2025 EST
Create and maintain Vertex AI Search data stores Earned gru 26, 2025 EST
Model Armor: Securing AI Deployments Earned gru 25, 2025 EST
AI Infrastructure: Networking Techniques Earned gru 25, 2025 EST
AI Infrastructure: Storage Options Earned gru 25, 2025 EST
AI Infrastructure: Deployment Types Earned gru 25, 2025 EST
AI Infrastructure: Cloud TPUs Earned gru 24, 2025 EST
AI Infrastructure: Cloud GPUs Earned gru 24, 2025 EST
AI Infrastructure: Introduction to AI Hypercomputer Earned gru 24, 2025 EST
Build generative virtual agents with API integrations Earned gru 23, 2025 EST
Implement Hybrid Search Earned gru 22, 2025 EST
Implement RAG with Vertex AI Earned gru 22, 2025 EST
Create Embeddings, Vector Search, and RAG with BigQuery Earned gru 19, 2025 EST
Prompt Design in Vertex AI Earned gru 13, 2025 EST
Build Generative AI Agents with Vertex AI and Flutter Earned lis 26, 2025 EST
Architect Customer Engagement Suite with Google AI Earned lis 25, 2025 EST
Introduction to Gemini Enterprise Earned lis 25, 2025 EST
Deploy an Agent with Agent Development Kit (ADK) Earned lis 20, 2025 EST
Deploy Multi-Agent Systems with Agent Development Kit (ADK) and Agent Engine Earned lis 19, 2025 EST

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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Complete the intermediate Engineer Data for Predictive Modeling with BigQuery ML skill badge to demonstrate skills in the following: building data transformation pipelines to BigQuery using Dataprep by Trifacta; using Cloud Storage, Dataflow, and BigQuery to build extract, transform, and load (ETL) workflows; and building machine learning models using BigQuery ML.

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Complete the Improve customer and agent satisfaction with Agent Assist skill badge to demonstrate your proficiency in configuring basic conversational agents that can escalate actions to human agents, and configuring Agent Assist to help human agents with customer queries. You prove your knowledge in configuring Generators for summarization, classification and recommendation of tickets as well leverage tools such as Generative Knowledge Assist, to provide further context to human 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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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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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 equips learners with the essential knowledge and practical tools to develop and implement artificial intelligence (AI) responsibly. Through an exploration of ethical considerations, best practices, and governance procedures, participants will gain an understanding of how to navigate the complex landscape of AI while upholding ethical standards and minimizing potential risks.

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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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With this course you will learn how to use different techniques to fine-tune Gemini. Model tuning is an effective way to customize large models like Gemini for your specific tasks. It's a key step to improve the model's quality and efficiency. This course will give an overview of model tuning, describe the tuning options available for Gemini, help you determine when each tuning option should be used and how to perform tuning.

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This course equips machine learning practitioners with the essential tools, techniques, and best practices for evaluating both generative and predictive AI models. Model evaluation is a critical discipline for ensuring that ML systems deliver reliable, accurate, and high-performing results in production. Participants will gain a deep understanding of various evaluation metrics, methodologies, and their appropriate application across different model types and tasks. The course will emphasize the unique challenges posed by generative AI models and provide strategies for tackling them effectively. By leveraging Google Cloud's Vertex AI platform, participants will learn how to implement robust evaluation processes for model selection, optimization, and continuous monitoring.

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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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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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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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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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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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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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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 explores the different products and capabilities of Gemini Enterprise for Customer Experience, including CX Agent Studio, Agent Assist and CX Insights. 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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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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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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Learn a variety of strategies and techniques to engineer effective prompts for generative models

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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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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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This course reviews the essential security features of Model Armor and equips you to work with the service. You’ll learn about the security risks associated with LLMs and how Model Armor protects your AI applications.

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Welcome to the "AI Infrastructure: Networking Techniques" course. In this course, you'll learn to leverage Google Cloud's high-bandwidth, low-latency infrastructure to optimize data transfer and communication between all the components of your AI system. By the end, you'll grasp the critical role networking plays across the entire AI pipeline from data ingestion and training to inference and be able to apply best practices to ensure your workloads run at maximum speed.

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In this course, you’ll take a comprehensive journey through the storage solutions available on Google Cloud, specifically tailored for AI and high-performance computing (HPC) workloads. You’ll learn how to choose the right storage for each stage of the ML lifecycle. You’ll explore how to optimize for I/O performance during training, manage massive datasets for data preparation, and serve model artifacts with low latency. Through practical examples and demonstrations, you’ll gain the expertise to design robust storage solutions that accelerate your AI innovation.

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This course provides a comprehensive guide to deploying, managing, and optimizing AI and high-performance computing (HPC) workloads on Google Cloud. Through a series of lessons and practical demonstrations, you’ll explore diverse deployment strategies, ranging from highly customizable environments using Google Compute Engine (GCE) to managed solutions like Google Kubernetes Engine (GKE). Specifically, you’ll learn how to create clusters and deploy GKE for inference.

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Welcome to the Cloud TPUs course. We'll explore the advantages and disadvantages of TPUs in various scenarios and compare different TPU accelerators to help you choose the right fit. You'll learn strategies to maximize performance and efficiency for your AI models and understand the significance of GPU/TPU interoperability for flexible machine learning workflows. Through engaging content and practical demos, we'll guide you step-by-step in leveraging TPUs effectively.

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Curious about the powerful hardware behind AI? This module 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 GPU for your AI projects, helping you make smart choices for your AI workloads.

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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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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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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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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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This course explores a Retrieval Augmented Generation (RAG) solution in BigQuery to mitigate AI hallucinations. It introduces a RAG workflow that encompasses creating embeddings, searching a vector space, and generating improved answers. The course explains the conceptual reasons behind these steps and their practical implementation with BigQuery. By the end of the course, learners will be able to build a RAG pipeline using BigQuery and generative AI models like Gemini and embedding models to address their own AI hallucination use cases.

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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 learn how to develop an app using Flutter, Google's portable UI toolkit, and integrate the app with Gemini, Google's family of generative AI models. You also use Vertex AI Agent Builder, Google's platform for building and managing AI Agents and applications.

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In this course, you'll dive deep into the essential topics you need to know to design, build, and maintain a powerful CES solution. Get ready to transform your understanding of what's possible and create an architecture that drives customer satisfaction. This course is designed to introduce you to the architecture of the Customer Engagement Suite (CES). You'll explore the main considerations for building and implementing Conversational AI solutions including key architectural components and integrations. You'll also explore how Conversational AI interacts with Vertex AI and get a high-level overview of the key features of the Conversational AI Platform.

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This course introduces Gemini Enteprise, a powerful platform that brings together AI agents, enterprise search, NotebookLM, and intelligent data access to solve organizational challenges. Through real-world examples and hands-on exploration, learners will be able to connect Gemini Enterprise capabilities to real business needs, describe its architecture, and explain how it handles data access and privacy across roles.

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In this challenge lab, you will demonstrate your ability to author agents using Agent Development Kit (ADK), deploy those agents to Agent Engine, and use them from a web app. Complete the challenge lab to earn a Google Cloud skill badge.

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In this course, you’ll learn to use the Google Agent Development Kit to build complex, multi-agent systems. You will build agents equipped with tools, and connect them with parent-child relationships and flows to define how they interact. You’ll run your agents locally and deploy them to Vertex AI Agent Engine to run as a managed agentic flow, with infrastructure decisions and resource scaling handled by Agent Engine. Please note these labs are based off a pre-released version of this product. There may be some lag on these labs as we provide maintenance updates.

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