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Yeswanth Vankayala

Member since 2021

Diamond League

26267 points
Evaluate and Improve Agent Development Kit Agents Earned יול 17, 2026 EDT
Evaluate Generative and Agentic Systems Earned יול 13, 2026 EDT
Secure Your Agents With Gemini Platform Earned יול 12, 2026 EDT
Govern agents with Agent Gateway, Agent Registry, and Policies Earned יול 8, 2026 EDT
Craft ADK Agents with Persistent Memories Earned יונ 30, 2026 EDT
Build Enterprise Agents with Code Execution on Gemini platform Earned יונ 17, 2026 EDT
Build Agents with the Agent Development Kit Earned יונ 16, 2026 EDT
Accelerate Knowledge Exchange with Gemini Enterprise Earned יונ 14, 2026 EDT
Plan Change Management for Gemini Deployments Earned יונ 13, 2026 EDT
Add Agents to Gemini Earned יונ 1, 2026 EDT
Accelerate Development with Antigravity Earned מאי 29, 2026 EDT
Accelerate App Development with Gemini CLI Earned מאי 29, 2026 EDT
Extend Gemini Capabilities Earned דצמ 11, 2025 EST
Empower Gen AI Apps with Tool Use Earned דצמ 10, 2025 EST
Engineer Effective Prompts for Generative Models Earned דצמ 8, 2025 EST
Explore Google's Gen AI Models Earned דצמ 5, 2025 EST
[DEPRECATED] Integrate Vertex AI Search and Conversation into Voice and Chat Apps Earned דצמ 4, 2025 EST
Deploy and Evaluate Model Garden Models Earned נוב 20, 2025 EST
Supervised Fine-tuning for Gemini Earned נוב 18, 2025 EST
[DEPRECATED] Model evaluation on Vertex AI Earned נוב 17, 2025 EST
Machine Learning Operations (MLOps) with Agent Platform: Model Evaluation Earned נוב 12, 2025 EST
Find, Explore and Deploy Model Garden Models Earned נוב 10, 2025 EST
Build search and recommendations applications with AI Applications Earned נוב 6, 2025 EST
Introduction to AI Applications Earned נוב 6, 2025 EST
Deploy an Agent with Agent Development Kit (ADK) Earned נוב 5, 2025 EST
Deploy Multi-Agent Systems with Gemini Enterprise Agent Platform Earned נוב 5, 2025 EST
Gen AI Apps: Transform Your Work Earned יונ 23, 2025 EDT
Gen AI: Navigate the Landscape Earned יונ 23, 2025 EDT
Gen AI: Unlock Foundational Concepts Earned יונ 3, 2025 EDT
Gen AI: Beyond the Chatbot Earned יונ 1, 2025 EDT
Developing a Google SRE Culture Earned אוק 26, 2023 EDT
[DEPRECATED] Generative AI Fundamentals Earned אוק 9, 2023 EDT
Introduction to Responsible AI - בעברית Earned ספט 29, 2023 EDT
Introduction to Large Language Models - בעברית Earned ספט 29, 2023 EDT
Create and Manage Bigtable Instances Earned אוג 30, 2023 EDT
Create and Manage Cloud Spanner Instances Earned אוג 26, 2023 EDT
Implement Cloud Security Fundamentals on Google Cloud Earned אוג 25, 2023 EDT
Create and Manage AlloyDB Instances Earned אוג 25, 2023 EDT
Migrate MySQL Data to Cloud SQL Using Database Migration Service Earned אוג 25, 2023 EDT
Implement Cloud Collaboration and Productivity Workflows Earned אוג 23, 2023 EDT
DEPRECATED Build and Deploy Machine Learning Solutions on Vertex AI Earned אוג 21, 2023 EDT
Build a Data Warehouse with BigQuery Earned אוג 19, 2023 EDT
Create ML Models with BigQuery ML Earned אוג 18, 2023 EDT
Develop Serverless Apps with Firebase Earned אוג 17, 2023 EDT
Develop Serverless Applications on Cloud Run Earned אוג 16, 2023 EDT
Build a Secure Google Cloud Network Earned אוג 15, 2023 EDT
Transformer Models and BERT Model - בעברית Earned אוג 14, 2023 EDT
Encoder-Decoder Architecture - בעברית Earned אוג 14, 2023 EDT
Attention Mechanism - בעברית Earned אוג 14, 2023 EDT
Introduction to Image Generation - בעברית Earned אוג 14, 2023 EDT
Introduction to Generative AI - בעברית Earned אוג 13, 2023 EDT
Build Infrastructure with Terraform on Google Cloud Earned אוג 13, 2023 EDT
Derive Insights from BigQuery Data Earned אוג 12, 2023 EDT
Monitor and Log with Google Cloud Observability Earned אוג 12, 2023 EDT
Deploy Kubernetes Applications on Google Cloud Earned אוג 11, 2023 EDT
Prepare Data for ML APIs on Google Cloud Earned אוג 9, 2023 EDT
Implement DevOps Workflows in Google Cloud Earned אוג 7, 2023 EDT
Set Up an App Dev Environment on Google Cloud Earned אוג 7, 2023 EDT
Understanding Google Cloud Security and Operations - בעברית Earned ספט 11, 2022 EDT
Infrastructure and Application Modernization with Google Cloud - בעברית Earned אוג 9, 2022 EDT
Innovating with Data and Google Cloud - בעברית Earned אוג 8, 2022 EDT
Digital Transformation with Google Cloud - בעברית Earned אוג 6, 2022 EDT
Migration Summit 2022 Earned אוג 3, 2022 EDT
Essential Google Cloud Infrastructure: Foundation Earned אפר 29, 2022 EDT
Intro to BigQuery: Analytics & Machine Learning Earned מרץ 13, 2022 EDT
Cloud Hero: Application Development Earned מרץ 12, 2022 EST
Cloud Hero Infra II Skills Earned מרץ 11, 2022 EST
Google Cloud Fundamentals: Core Infrastructure Earned פבר 25, 2022 EST

Complete the Evaluate and Improve Agent Development Kit Agents skill badge to demonstrate your ability to use ADK's evaluation tools to "hill climb" — making measurable, iterative improvements to an agent. You will run an initial evaluation to establish a baseline, apply optimization techniques, and re-evaluate the agent to measure your success.

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Deploying reliable generative and agentic systems requires moving beyond traditional software testing and subjective assessments. Because these probabilistic systems introduce unique failure modes, technical teams need structured frameworks to measure quality, enforce guardrails, and tie model behavior directly to business outcomes. Evaluate Generative and Agentic Systems gives engineers and architects practical tools to design, execute, and scale rigorous evaluation pipelines. You will master the full evaluation hierarchy, from fast deterministic checks to calibrated Large Language Model (LLM) autoraters and human-in-the-loop workflows. Learn how to simulate high-fidelity test datasets, trace multi-turn agent trajectories, and establish reliable consistency metrics across runs. By the end of this course, you will be prepared to translate organizational Key Performance Indicators (KPIs) into actionable technical benchmarks, calibrate objective rubrics, and maintain a continuous quali…

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Welcome to Secure your Agents with Gemini Platform, a defense-in-depth course designed to help you establish centralized, zero-trust control over your AI workforce. You will learn to deploy Agent Gateway and Model Armor to manage agent sprawl, implement secure identity protocols like delegated identity and multi-legged OAuth flows, and set up advanced monitoring and observability. By the end, you will have the practical skills to build a secure, well-governed, and scalable agent ecosystem ready for enterprise-grade production. Explore other content in the Gemini Agent Ready (GEAR) program: https://developers.google.com/program/gear.

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This course covers the technical side of governing an AI agent workforce using Agent Gateway, Agent Registry, and Policies. It discusses the overall architecture of centralized agent governance, focusing on managing agent sprawl and securing communication with internal systems. The course explores the infrastructure and enterprise networking required to deploy the Agent Gateway, including automated deployment with Terraform and configuring private egress via Private Service Connect. It offers guidance towards establishing a zero-trust security perimeter using unique agent identities with mTLS, per-tool authorization with REQUEST_AUTHZ, and granular CEL policies. Furthermore, it empowers administrators to implement content security guardrails using Model Armor in Gemini platform and Cloud DLP to mitigate semantic threats like prompt injection and data leakage. Finally, the course explains how to manage tool discovery through the Agent Registry and ensure end-to-end observability and thr…

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This course explores architecting stateful AI agents that use short-term memory within a session or long-term memory services. Additionally, it covers giving agents existing expertise through skills. It details the core pillars of context engineering to facilitate personalized, continuous conversational experiences with expert agents.

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In this course, you explore the Code Execution feature of the Gemini platform, which lets AI agents safely generate and run Python code in isolated sandbox environments. You learn how the Agent Sandbox fits into the broader platform architecture, how to configure and operate Code Execution sandboxes using the Agent Platform SDK, and how to integrate code execution into agent workflows with the Agent Development Kit (ADK) in Gemini platform.

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In this course, you'll learn to use the Agent Development Kit (ADK) in Gemini platform to build systems where multiple AI agents collaborate on complex tasks. You'll start with the ADK agent model: how ADK represents agents, tools, and runners, and how a single agent is configured and run. You'll then make tools the model can call, persist session state across agents, and instrument the execution lifecycle with callbacks and plugins. Next, you'll orchestrate multiple agents using ADK's template workflow agents and graph-based workflows, and ground them in enterprise data through multi-source retrieval and MCP integrations. Finally, you'll deploy a multi-agent system to Agent Runtime as a managed service and register and share it through Gemini so users across an organization can reach it.

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Unite Google’s expertise in search and AI with Gemini Enterprise, a powerful tool designed to help employees find specific information from document storage, email, chats, ticketing systems, and other data sources, all from a single search bar. The Gemini Enterprise assistant can also help brainstorm, research, outline documents, and take actions like inviting coworkers to a calendar event to accelerate knowledge work and collaboration of all kinds. (Please note Gemini Enterprise was previously named Google Agentspace, there may be references to the previous product name in this course.)

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This course will guide you through designing customer engagements that result in successful, well-utilized Gemini deployments. Study the art of change management, how to identify and engage sponsors, recruit early adopters, help your customer identify and address cultural change challenges and skills gaps, and effectively deliver project communications. Additionally, learn how to support your customer to plan, offer, conduct, and evaluate end-user training, leverage partner resources, and create essential project documents.

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In this challenge lab, you will demonstrate your ability to add agents to Gemini. You will build an agent with Agent Designer, and you will build a no-code agent with Agent Development Kit, deploy it to Agent Engine, and add it to Gemini.

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Complete the Accelerate Development with Antigravity skill badge to demonstrate your proficiency in using the Antigravity IDE for developing agentic workflows. You will be tasked with configuring an MCP server, authoring custom agent skills and rules, prototyping with the Agents CLI, and deploying to the Google Cloud Agent Runtime. 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 is designed for app developers and DevOps engineers who want to work smarter by using Gemini CLI, a generative AI agent made for the terminal and powered by Gemini. This course discusses Gemini CLI installation and configuration, and introduces use cases and security best practices. It explains commands, tools, MCP servers, and extensions. With a hands-on exercise, you'll install and configure Gemini CLI and use it to analyze code and build and modify an app.

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Complete the Extend Gemini Capabilities skill badge to demonstrate your ability to extend Gemini capabilities with actions, grounding with Google Search, and a conversational agent. 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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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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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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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 skill bagde, you will demonstrate your ability to use and compare models available in the Vertex AI Model Garden. You'll deploy a model to a Vertex AI Endpoint, query other models via their API, and use Vertex AI's Gen AI evaluation service to measure the performance of multiple models.

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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 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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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 Agent Platform, participants will learn how to implement robust evaluation processes for model selection, optimization, and continuous monitoring.

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Model Garden on Gemini platform 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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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 introduces AI Applications. You will learn about the types of apps that you can create using AI Applications, the high-level steps that its data stores automate for you, and what advanced features can be enabled for Search apps. (Please note Gemini Enterprise was previously named Google Agentspace, there may be references to the previous product name in this course.)

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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 Agent Runtime on Gemini Enterprise Agent Platform to run as a managed agentic flow, with infrastructure decisions and resource scaling handled by Agent Runtime. 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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Transform Your Work With Gen AI Apps is the fourth course of the Gen AI Leader learning path. This course introduces Google’s gen AI applications, such as Google Workspace with Gemini and Gemini Notebook. It guides you through concepts like grounding, retrieval augmented generation, constructing effective prompts and building automated workflows.

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Gen AI: Navigate the Landscape is the third course of the Gen AI Leader learning path. Gen AI is changing how we work and interact with the world around us. But as a leader, how can you harness its power to drive real business outcomes? In this course, you explore the different layers of building gen AI solutions, Google Cloud’s offerings, and the factors to consider when selecting a solution.

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Gen AI: Unlock Foundational Concepts is the second course of the Gen AI Leader learning path. In this course, you unlock the foundational concepts of generative AI by exploring the differences between AI, ML, and gen AI, and understanding how various data types enable generative AI to address business challenges. You also gain insights into Google Cloud strategies to address the limitations of foundation models and the key challenges for responsible and secure AI development and deployment.

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Gen AI: Beyond the Chatbot is the first course of the Gen AI Leader learning path and has no prerequisites. This course aims to move beyond the basic understanding of chatbots to explore the true potential of generative AI for your organization. You explore concepts like foundation models and prompt engineering, which are crucial for leveraging the power of gen AI. The course also guides you through important considerations you should make when developing a successful gen AI strategy for your organization.

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In many IT organizations, incentives are not aligned between developers, who strive for agility, and operators, who focus on stability. Site reliability engineering, or SRE, is how Google aligns incentives between development and operations and does mission-critical production support. Adoption of SRE cultural and technical practices can help improve collaboration between the business and IT. This course introduces key practices of Google SRE and the important role IT and business leaders play in the success of SRE organizational adoption.

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Earn a skill badge 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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זהו קורס מבוא ממוקד שמטרתו להסביר מהי אתיקה של בינה מלאכותית, למה היא חשובה ואיך Google נוהגת לפי כללי האתיקה של הבינה המלאכותית במוצרים שלה. מוצגים בו גם 7 עקרונות ה-AI של Google.

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זהו קורס מבוא ממוקד שבוחן מהם מודלים גדולים של שפה (LLM), איך משתמשים בהם בתרחישים שונים לדוגמה ואיך אפשר לשפר את הביצועים שלהם באמצעות כוונון של הנחיות. הוא גם כולל הסבר על הכלים של Google שיעזרו לכם לפתח אפליקציות בינה מלאכותית גנרטיבית משלכם.

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Complete the introductory Create and Manage Bigtable Instances skill badge to demonstrate skills in the following: creating instances, designing schemas, querying data, and performing administrative tasks in Bigtable including monitoring performance and configuring node autoscaling and replication.

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Complete the introductory Create and Manage Cloud Spanner Instances skill badge to demonstrate skills in the following: creating and interacting with Cloud Spanner instances and databases; loading Cloud Spanner databases using various techniques; backing up Cloud Spanner databases; defining schemas and understanding query plans; and deploying a Modern Web App connected to a Cloud Spanner instance.

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Complete the intermediate Implement Cloud Security Fundamentals on Google Cloud skill badge course to demonstrate skills in the following: creating and assigning roles with Identity and Access Management (IAM); creating and managing service accounts; enabling private connectivity across virtual private cloud (VPC) networks; restricting application access using Identity-Aware Proxy; managing keys and encrypted data using Cloud Key Management Service (KMS); and creating a private Kubernetes cluster.

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Complete the introductory Create and Manage AlloyDB Instances skill badge to demonstrate skills in the following: performing core AlloyDB operations and tasks, migrating to AlloyDB from PostgreSQL, administering an AlloyDB database, and accelerating analytical queries using the AlloyDB Columnar Engine.

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Complete the introductory Migrate MySQL Data to Cloud SQL Using Database Migration Service skill badge course to demonstrate skills in the following: migrating MySQL data to Cloud SQL using different job types and connectivity options available in Database Migration Service and migrating MySQL user data when running Database Migration Service jobs.

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Earn an introductory skill badge by completing the Implement Cloud Collaboration and Productivity Workflows course, where you will get introduced to Google's collaborative platform and learn to use Gmail, Calendar, Meet, Drive, Sheets, and AppSheet.

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Earn the intermediate skill badge by completing the Build and Deploy Machine Learning Solutions on Vertex AI skill badge course, where you learn how to use Google Cloud's Vertex AI platform, AutoML, and custom training services to train, evaluate, tune, explain, and deploy machine learning models.

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Complete the intermediate Build a Data Warehouse with BigQuery skill badge course to demonstrate skills in the following: joining data to create new tables, troubleshooting joins, appending data with unions, creating date-partitioned tables, and working with JSON, arrays, and structs in BigQuery.

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Complete the intermediate Create ML Models with BigQuery ML skill badge to demonstrate skills in creating and evaluating machine learning models with BigQuery ML to make data predictions.

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Complete the intermediate Develop Serverless Apps with Firebase skill badge course to demonstrate skills in the following: architecting and building serverless web applications with Firebase, utilizing Firestore for database management, automating deployment processes using Cloud Build, and integrating Google Assistant functionality into your applications.

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Complete the intermediate Develop Serverless Applications on Cloud Run skill badge course to demonstrate skills in the following: integrating Cloud Run with Cloud Storage for data management, architecting resilient asynchronous systems using Cloud Run and Pub/Sub, constructing REST API gateways powered by Cloud Run, and building and deploying services on Cloud Run.

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Earn a skill badge by completing the Build a Secure Google Cloud Network skill badge course, where you will learn about multiple networking-related resources to build, scale, and secure your applications on Google Cloud.

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בקורס הזה נציג את הארכיטקטורה של טרנספורמרים ואת המודל של ייצוגים דו-כיווניים של מקודד מטרנספורמרים (BERT). תלמדו על החלקים השונים בארכיטקטורת הטרנספורמר, כמו מנגנון תשומת הלב, ועל התפקיד שלו בבניית מודל BERT. תלמדו גם על המשימות השונות שאפשר להשתמש ב-BERT כדי לבצע אותן, כמו סיווג טקסטים, מענה על שאלות והֶקֵּשׁ משפה טבעית. נדרשות כ-45 דקות כדי להשלים את הקורס הזה.

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בקורס הזה לומדים בקצרה על ארכיטקטורת מקודד-מפענח, ארכיטקטורה עוצמתית ונפוצה ללמידת מכונה שמשתמשים בה במשימות של רצף לרצף, כמו תרגום אוטומטי, סיכום טקסט ומענה לשאלות. תלמדו על החלקים השונים בארכיטקטורת מקודד-מפענח, איך לאמן את המודלים האלה ואיך להשתמש בהם. בהדרכה המפורטת המשלימה בשיעור ה-Lab תקודדו ב-TensorFlow תרחיש שימוש פשוט בארכיטקטורת מקודד-מפענח: כתיבת שיר מאפס.

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בקורס נלמד על מנגנון תשומת הלב, שיטה טובה מאוד שמאפשרת לרשתות נוירונים להתמקד בחלקים ספציפיים ברצף הקלט. נלמד איך עובד העיקרון של תשומת הלב, ואיך אפשר להשתמש בו כדי לשפר את הביצועים במגוון משימות של למידת מכונה, כולל תרגום אוטומטי, סיכום טקסט ומענה לשאלות.

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בקורס נלמד על מודלים של דיפוזיה, משפחת מודלים של למידת מכונה שיצרו הרבה ציפיות לאחרונה בתחום של יצירת תמונות. מודלים של דיפוזיה שואבים השראה מפיזיקה, וספציפית מתרמודינמיקה. בשנים האחרונות, מודלים של דיפוזיה הפכו לפופולריים גם בתחום המחקר וגם בתעשייה. מודלים של דיפוזיה עומדים מאחורי הרבה מהכלים והמודלים החדשניים ליצירת תמונות ב-Google Cloud. בקורס הזה נלמד על התיאוריה שמאחורי מודלים של דיפוזיה, ואיך לאמן ולפרוס אותם ב-Vertex AI.

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זהו קורס מבוא ממוקד שמטרתו להסביר מהי בינה מלאכותית גנרטיבית, איך משתמשים בה ובמה היא שונה משיטות מסורתיות של למידת מכונה. הוא גם כולל הסבר על הכלים של Google שיעזרו לכם לפתח אפליקציות בינה מלאכותית גנרטיבית משלכם.

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Complete the intermediate Build Infrastructure with Terraform on Google Cloud skill badge to demonstrate skills in the following: Infrastructure as Code (IaC) principles using Terraform, provisioning and managing Google Cloud resources with Terraform configurations, effective state management (local and remote), and modularizing Terraform code for reusability and organization.

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Complete the introductory Derive Insights from BigQuery Data skill badge course to demonstrate skills in the following: Write SQL queries.Query public tables.Load sample data into BigQuery.Troubleshoot common syntax errors with the query validator in BigQuery.Create reports in Data Studio by connecting to BigQuery data.

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Complete the introductory Monitor and Log with Google Cloud Observability skill badge course to demonstrate skills in the following: monitoring virtual machines in Compute Engine, utilizing Cloud Monitoring for multi-project oversight, extending monitoring and logging capabilities to Cloud Functions, creating and sending custom application metrics, and configuring Cloud Monitoring alerts based on custom metrics.

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Complete the intermediate Deploy Kubernetes Applications on Google Cloud skill badge course to demonstrate skills in the following: Configuring and building Docker container images.Creating and managing Google Kubernetes Engine (GKE) clusters.Utilizing kubectl for efficient cluster management.Deploying Kubernetes applications with robust continuous delivery (CD) practices.

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Complete the introductory Prepare Data for ML APIs on Google Cloud skill badge to demonstrate skills in the following: cleaning data with Dataprep by Trifacta, running data pipelines in Dataflow, creating clusters and running Apache Spark jobs in Managed Service for Apache Spark, and calling ML APIs including the Cloud Natural Language API, Google Cloud Speech-to-Text API, and Video Intelligence API.

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Complete the intermediate Implement DevOps Workflows in Google Cloud skill badge to demonstrate skills in the following: creating and managing Git repositories, launching, managing, and scaling deployments on Google Kubernetes Engine (GKE), and architecting CI/CD pipelines that automate container image builds and deployments to GKE.

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Earn a skill badge by completing the Set Up an App Dev Environment on Google Cloud skill badge course, where you learn how to build and connect storage-centric cloud infrastructure using the basic capabilities of the following technologies: Cloud Storage, Identity and Access Management, Cloud Functions, and Pub/Sub.

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הקורס בוחן ניהול עלויות, אבטחה ותפעול בענן. ראשית, מוסבר איך עסקים יכולים לרכוש שירותי IT מספק שירותי ענן ולשמר חלק מהתשתית שלהם או לבחור לא לשמר אותה בכלל. שנית, הקורס מתאר איך האחריות על אבטחת נתונים מתחלקת בין ספק שירותי הענן לעסק, וסוקר את אבטחת ההגנה לעומק (defense-in-depth) שמובנית ב-Google Cloud. לבסוף, הקורס מתייחס לכך שצוותי IT ומנהלי העסק צריכים לשנות את החשיבה על ניהול משאבי IT בענן, ונוגע באופן שבו כלי ניטור המשאבים ב-Google Cloud יכולים לסייע להם לשמור על שליטה וניראות בסביבת הענן שלהם.

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בארגונים מסורתיים רבים משתמשים במערכות ובאפליקציות מדורות קודמים, וקשה לבצע באמצעותן התאמה לעומס ופעולות מהירות הדרושות כדי לעמוד בציפיות מודרניות של לקוחות. מנהיגים עסקיים וקובעי מדיניות IT צריכים כל הזמן לבחור בין תחזוקה של מערכות מדורות קודמים לבין השקעה במוצרים ובשירותים חדשים. בקורס הזה נבחן את האתגרים הנובעים משימוש בתשתית IT מיושנת, ואיך בעלי עסקים יכולים לבצע מודרניזציה של תשתיות בעזרת טכנולוגיית ענן. הקורס מתחיל בהבנה מעמיקה של אפשרויות המחשוב השונות הזמינות בענן ופירוט היתרונות של כל אחת מהאפשרויות. לאחר מכן נבחן את האפשרויות למודרניזציה של האפליקציות ושל ממשקי API (ממשק תכנות יישומים). בקורס מתוארים גם מגוון פתרונות של Google Cloud שיכולים לשפר את תהליך פיתוח המערכות וניהולן בעסקים שונים, כמו Compute Engine,‏ App Engine ו-Apigee.

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טכנולוגיית הענן לבדה מספקת לעסק חלק קטן בלבד מהערך האמיתי שלה. כשהיא משולבת עם נתונים בנפח רב מאוד, נוצרת העוצמה שמאפשרת להפיק ערך וליצור חוויות חדשות ללקוחות. במסגרת הקורס הזה תלמדו מהם נתונים, איך השתמשו בהם בעבר בחברות לצורך קבלת החלטות ולמה הם קריטיים כל כך ללמידה חישובית. בנוסף, בקורס הזה יוצגו ללומדים מושגים טכניים כמו נתונים מובְנים ולא מובְנים, מסד נתונים, מחסן נתונים (data warehouse) ואגמי נתונים (data lakes). בהמשך, הקורס יעסוק במוצרי Google Cloud הנפוצים ביותר בתחום הנתונים, ובמוצרים כאלה ששיעור השימוש בהם גדל במהירות הרבה ביותר.

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מהי טכנולוגיית ענן ומהו מדע הנתונים? וחשוב יותר, איך הם יכולים לעזור לכם, לצוות שלכם ולעסק שלכם? קורס המבוא הזה בנושא טרנספורמציה דיגיטלית מתאים למי שרוצה ללמוד על טכנולוגיית הענן כדי להתמקצע ולהצטיין בעבודתו וכדי לעזור בפיתוח העתיד של העסק. בקורס יוגדרו מונחי יסוד כגון הענן, נתונים וטרנספורמציה דיגיטלית. בנוסף, נבחן דוגמאות של חברות מרחבי העולם שמשתמשות בטכנולוגיית הענן כדי לבצע טרנספורמציה בעסק. הקורס כולל סקירה של סוגי ההזדמנויות שיש לחברות ושל האתגרים הנפוצים שחברות מתמודדות איתם במהלך טרנספורמציה דיגיטלית. הקורס גם מדגים איך עמודי התווך של פתרונות Google Cloud יכולים לעזור בתהליך. חשוב לומר: טרנספורמציה דיגיטלית לא קשורה רק לשימוש בטכנולוגיות חדשות. כדי הטרנספורמציה תהיה מלאה, ארגונים צריכים גם ליישם חדשנות ולפתח דפוס חשיבה שמקדם חדשנות בכל התחומים והצוותים. השיטות המומלצות המתוארות בקורס יעזרו לכם להשיג את המטרה הזו.

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The GSI Migration Summit is a curated collection of hands-on labs and quests that provide coverage of application migration. Security and analytics labs and quests have been added to provide coverage of key platform concepts and services.

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This accelerated on-demand course introduces participants to the comprehensive and flexible infrastructure and platform services provided by Google Cloud with a focus on Compute Engine. Through a combination of video lectures, demos, and hands-on labs, participants explore and deploy solution elements, including infrastructure components such as networks, virtual machines and applications services. You will learn how to use the Google Cloud through the console and Cloud Shell. You'll also learn about the role of a cloud architect, approaches to infrastructure design, and virtual networking configuration with Virtual Private Cloud (VPC), Projects, Networks, Subnetworks, IP addresses, Routes, and Firewall rules.

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Welcome Gamers! Today's game is all about experimenting with Big Query for Machine Learning! Use real life case studies to learn various concepts of BQML and have fun. Take labs to earn points. The faster you complete the lab objectives, the higher your score.

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Learn how the Google Cloud ecosystem can help you build secure, scalable and intelligent cloud native applications and experience seamless integration between Google services and APIs to create intelligent apps. Dive deep into key solutions to common use cases and gain hands-on experience with App Engine, plus score points and compete with other players in today's game.

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Get hands-on practice with Google Cloud! You will compete with your peers to see who can finish this game with the most points. Speed and accuracy will be used to calculate your scores — earn points by completing the labs accurately and bonus points for speed! Be sure to click “End” where you’re done with each lab to be rewarded your points.

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Google Cloud Fundamentals: Core Infrastructure introduces important concepts and terminology for working with Google Cloud. Through videos and hands-on labs, this course presents and compares many of Google Cloud's computing and storage services, along with important resource and policy management tools.

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