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Kulwant Singh

Member since 2022

Gold League

30396 points
Discover Business Value for Customers Earned Dec 11, 2025 EST
Building Gen AI Apps with Vertex AI: Prompting and Tuning Earned Apr 2, 2025 EDT
Empower Gen AI Apps with Tool Use Earned Apr 2, 2025 EDT
Explore Generative AI with the Gemini API in Vertex AI Earned Apr 2, 2025 EDT
Develop Advanced Enterprise Search and Conversation Applications Earned Apr 1, 2025 EDT
Custom Search with Embeddings in Vertex AI Earned Apr 1, 2025 EDT
Boost Productivity with Gemini in BigQuery Earned Mar 31, 2025 EDT
Responsible AI: Applying AI Principles with Google Cloud Earned Mar 31, 2025 EDT
Introduction to Responsible AI Earned Mar 31, 2025 EDT
Vector Search and Embeddings Earned Mar 31, 2025 EDT
Implementing Generative AI with Vertex AI Earned Mar 31, 2025 EDT
Generative AI for Business Leaders Earned Mar 31, 2025 EDT
Create Image Captioning Models Earned Mar 31, 2025 EDT
Introduction to Image Generation Earned Mar 31, 2025 EDT
Transformer Models and BERT Model Earned Mar 31, 2025 EDT
Encoder-Decoder Architecture Earned Mar 31, 2025 EDT
Attention Mechanism Earned Mar 18, 2025 EDT
Data Warehousing for Partners: Enable Google Cloud Customers Earned Mar 5, 2025 EST
Text Prompt Engineering Techniques Earned Feb 27, 2025 EST
Introduction to Generative AI Earned Feb 27, 2025 EST
Monitor and Log with Google Cloud Observability Earned Dec 26, 2024 EST
Google Cloud VMware Engine Fundamentals Earned Dec 19, 2024 EST
Migrating VMware to Google Cloud VMware Engine Earned Dec 19, 2024 EST
Generative AI Fundamentals Earned Aug 28, 2023 EDT
Introduction to Vertex AI Studio Earned Jun 30, 2023 EDT
Cloud Foundations: Customer Onboarding Best Practices Earned Jun 5, 2023 EDT
VM Migration for Partners Earned Aug 23, 2022 EDT
Inside Track: Install, Deploy and Configure Actifio GO Solutions Earned Jul 21, 2022 EDT
Migration Summit 2022 Earned Jul 19, 2022 EDT
VM Migration Earned Jul 15, 2022 EDT
Scaling with Google Cloud Operations Earned Jun 29, 2022 EDT
Modernize Infrastructure and Applications with Google Cloud Earned Jun 29, 2022 EDT
Build Infrastructure with Terraform on Google Cloud Earned Jun 29, 2022 EDT
Google Cloud Fundamentals: Core Infrastructure Earned Jun 23, 2022 EDT
Set Up an App Dev Environment on Google Cloud Earned Jun 23, 2022 EDT
VM Migration for Partners Earned Jun 22, 2022 EDT
Implementing Cloud Load Balancing for Compute Engine Earned May 26, 2022 EDT
Cloud Engineering Earned May 19, 2022 EDT
Google Cloud Essentials Earned May 17, 2022 EDT

The course aims to train Google technical sales partners on the business value discovery process using proprietary content. Course activities use an external tool (Yoodli). Refer to Yoodli's Terms of Service and Privacy Notice. Note: The Yoodli Labs in this course will be deprecated on March 31st. We encourage you to finish your remaining Yoodli Labs before the March 31 deadline.

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(This course was previously named Multimodal Prompt Engineering with Gemini and PaLM) This course teaches how to use Vertex AI Studio, a Google Cloud console tool for rapidly prototyping and testing generative AI models. You learn to test sample prompts, design your own prompts, and customize foundation models to handle tasks that meet your application's needs. Whether you are looking for text, chat, code, image or speech generative experiences Vertex AI Studio offers you an interface to work with and APIs to integrate your production application.

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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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Complete the intermediate Explore Generative AI with the Gemini API in Vertex AI skill badge to demonstrate skills in text generation, image and video analysis for enhanced content creation, and applying function calling techniques within the Gemini API. Discover how to leverage sophisticated Gemini techniques, explore multimodal content generation, and expand the capabilities of your AI-powered projects.

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In this course, you'll use text embeddings for tasks like classification, outlier detection, text clustering and semantic search. You'll combine semantic search with the text generation capabilities of an LLM to build Retrieval Augmented Generation (RAG) solutions, such as for question-answering systems, using Google Cloud's Vertex AI and Google Cloud databases.

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This course explores Google Cloud technologies to create and generate embeddings. Embeddings are numerical representations of text, images, video and audio, and play a pivotal role in many tasks that involve the identification of similar items, like Google searches, online shopping recommendations, and personalized music suggestions. Specifically, you’ll use embeddings for tasks like classification, outlier detection, clustering and semantic search. You’ll combine semantic search with the text generation capabilities of an LLM to build Retrieval Augmented Generation (RAG) systems and question-answering solutions, on your own proprietary data using Google Cloud’s Vertex AI.

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This course explores Gemini in BigQuery, a suite of AI-driven features to assist data-to-AI workflow. These features include data exploration and preparation, code generation and troubleshooting, and workflow discovery and visualization. Through conceptual explanations, a practical use case, and hands-on labs, the course empowers data practitioners to boost their productivity and expedite the development pipeline.

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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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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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Explore AI-powered search technologies, tools, and applications in this course. Learn semantic search utilizing vector embeddings, hybrid search combining semantic and keyword approaches, and retrieval-augmented generation (RAG) minimizing AI hallucinations as a grounded AI agent. Gain practical experience with Vertex AI Vector Search to build your intelligent search engine.

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This course will help ML Engineers, Developers, and Data Scientists implement Large Language Models for Generative AI use cases with Vertex AI. The first two modules of this course contain links to videos and prerequisite course materials that will build your knowledge foundation in Generative AI. Please do not skip these modules. The advanced modules in this course assume you have completed these earlier modules.

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A Business Leader in Generative AI can articulate the capabilities of core cloud Generative AI products and services and understand how they benefit organizations. This course provides an overview of the types of opportunities and challenges that companies often encounter in their digital transformation journey and how they can leverage Google Cloud's generative AI products to overcome these challenges.

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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 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 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 discusses the key elements of Google's Data Warehouse solution portfolio and strategy.

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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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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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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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In this course, you will start by building a solid foundation in core Google Cloud VMware Engine (GCVE) concepts. From there, you'll dive into the unique architecture and components of GCVE, exploring how to prepare, deploy, and investigate VMs in a private cloud environment. The course will then guide you through managing and monitoring your private cloud, configuring networking and storage components, and connecting GCVE to the broader ecosystem of Google Cloud services and the internet.

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This course educates partners on key concepts around deploying Google Cloud VMware Engine (GCVE) and leveraging HCX to migrate VMs from on-premises VMware to GCVE.

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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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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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The Cloud Foundations Customer Onboarding: Best Practices course enables partners to onboard customers on Google Cloud efficiently and in minimum time, by imparting knowledge, IP, and best practices from the Technical Onboarding Center (TOC) team at Global Delivery Center (GDC). The course explores Cloud Identity and organization, users and groups, administrative access, and resource hierarchy. It also examines network configuration, hybrid connectivity, logging and monitoring, and organizational security.

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This course provides comprehensive skills on VM migration, from the initial assessment through the final implementation through presentations, demonstrations, and whiteboard session.

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Actifio GO for Google Cloud is a SaaS offering which enables powerful enterprise class backup and recovery for Google Cloud resident and on-premises workloads. Actifio now supports backup, disaster recovery and rapid database cloning of Oracle on Bare Metal Solution on Google Cloud besides other enterprise workloads including SAP HANA, SQL Server, and others. This course provides a deep dive into at the preparation and deployment of the Actifio GO solution and its constituent components. Each module contains demos and explanations of each component. The Actifio GO training was originally designed for and only made available to Google Teams, however we’ve recognized how beneficial it would be for our Partners and are now offering our Partners exclusive access to the Actifio training and products, so they can benefit from the demos and best practices and bring them to their Google Cloud Customers.

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

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Google Cloud’s four step structured Cloud Migration Path Methodology provides a defined and repeatable path for users to follow when migrating and modernizing Virtual Machines. In this quest, you will get hands-on practice with Google’s current solution set for VM assessment, planning, migration, and modernization. You will start by analyzing your lab environment and building assessment reports with CloudPhysics and StratoZone, then build a landing zone within Google Cloud leveraging Terraform’s infrastructure-as-code templates, next you will manually transform a two-tier application into a cloud-native workload running on Kubernetes, and finally, transform a VM workload into Kubernetes with Migrate for Anthos and migrate a VM between cloud environments.

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Organizations of all sizes are embracing the power and flexibility of the cloud to transform how they operate. However, managing and scaling cloud resources effectively can be a complex task. Scaling with Google Cloud Operations explores the fundamental concepts of modern operations, reliability, and resilience in the cloud, and how Google Cloud can help support these efforts. Part of the Cloud Digital Leader learning path, this course aims to help individuals grow in their role and build the future of their business.

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Many traditional enterprises use legacy systems and applications that can't stay up-to-date with modern customer expectations. Business leaders often have to choose between maintaining their aging IT systems or investing in new products and services. "Modernize Infrastructure and Applications with Google Cloud" explores these challenges and offers solutions to overcome them by using cloud technology. Part of the Cloud Digital Leader learning path, this course aims to help individuals grow in their role and build the future of their business.

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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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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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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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This course provides comprehensive skills on VM migration, from the initial assessment through the final implementation through presentations, demonstrations, and whiteboard sessions.

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Complete the introductory Implementing Cloud Load Balancing for Compute Engine skill badge to demonstrate skills in the following: creating and deploying virtual machines in Compute Engine and configuring network and application load balancers.

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This fundamental-level quest is unique amongst the other quest offerings. The labs have been curated to give IT professionals hands-on practice with topics and services that appear in the Google Cloud Certified Associate Cloud Engineer Certification. From IAM, to networking, to Kubernetes engine deployment, this quest is composed of specific labs that will put your Google Cloud knowledge to the test. Be aware that while practice with these labs will increase your skills and abilities, we recommend that you also review the exam guide and other available preparation resources.

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In this introductory-level course, you get hands-on practice with the Google Cloud’s fundamental tools and services. Optional videos are provided to provide more context and review for the concepts covered in the labs. Google Cloud Essentials is a recommendeded first course for the Google Cloud learner - you can come in with little or no prior cloud knowledge, and come out with practical experience that you can apply to your first Google Cloud project. From writing Cloud Shell commands and deploying your first virtual machine, to running applications on Kubernetes Engine or with load balancing, Google Cloud Essentials is a prime introduction to the platform’s basic features.

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