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Pallavi Patil

Member since 2021

Silver League

15515 points
Build and Deploy a Generative AI solution using a RAG framework Earned أكتوبر 18, 2024 EDT
[DEPRECATED] Integrate Vertex AI Search and Conversation into Voice and Chat Apps Earned سبتمبر 19, 2024 EDT
App Deployment, Debugging, and Performance Earned يونيو 12, 2024 EDT
Hybrid Cloud Modernizing Applications with Anthos Earned يونيو 11, 2024 EDT
API Design and Fundamentals with the Apigee API Platform Earned ديسمبر 18, 2023 EST
Text Prompt Engineering Techniques Earned سبتمبر 21, 2023 EDT
Generative AI Explorer : Vertex AI Earned سبتمبر 19, 2023 EDT
[DEPRECATED] Implementing Generative AI with Vertex AI Earned سبتمبر 14, 2023 EDT
[DEPRECATED] Search with AI Applications Earned سبتمبر 14, 2023 EDT
Responsible AI: Applying AI Principles with Google Cloud Earned سبتمبر 14, 2023 EDT
Create Image Captioning Models Earned سبتمبر 14, 2023 EDT
Introduction to Vertex AI Studio Earned سبتمبر 14, 2023 EDT
[DEPRECATED]Introduction to Responsible AI Earned أغسطس 21, 2023 EDT
Transformer Models and BERT Model Earned أغسطس 21, 2023 EDT
Encoder-Decoder Architecture Earned أغسطس 21, 2023 EDT
[DEPRECATED] Generative AI Fundamentals Earned أغسطس 21, 2023 EDT
Attention Mechanism Earned أغسطس 21, 2023 EDT
Introduction to Large Language Models Earned أغسطس 21, 2023 EDT
Introduction to Image Generation Earned أغسطس 21, 2023 EDT
Introduction to Generative AI Earned أغسطس 3, 2023 EDT
Smart Analytics, Machine Learning, and AI on Google Cloud Earned نوفمبر 3, 2022 EDT
Serverless Data Processing with Dataflow: Foundations Earned نوفمبر 1, 2022 EDT
Measure Site Reliability using Cloud Operations Suite Earned سبتمبر 13, 2022 EDT
Logging and Monitoring in Google Cloud Earned سبتمبر 12, 2022 EDT
Certification Learning Path: Professional Cloud DevOps Engineer Earned سبتمبر 6, 2022 EDT
Monitor and Log with Google Cloud Observability Earned سبتمبر 5, 2022 EDT
Certification Learning Path: Professional Cloud DevOps Engineer Earned أغسطس 27, 2022 EDT
Developing a Google SRE Culture Earned أغسطس 23, 2022 EDT
Upgrading and Monitoring the Apigee Hybrid API Platform Earned أغسطس 11, 2022 EDT
Architecting and Installing the Apigee Hybrid API Platform Earned أغسطس 8, 2022 EDT
Certification Learning Path: Professional Cloud DevOps Engineer Earned يوليو 8, 2022 EDT
Build Batch Data Pipelines on Google Cloud Earned يونيو 30, 2022 EDT
Build Data Lakes and Data Warehouses on Google Cloud Earned يونيو 24, 2022 EDT
[DEPRECATED] Google Cloud Big Data and Machine Learning Fundamentals Earned يونيو 20, 2022 EDT
Build a Certification Study Guide: ACE Exam Prep Earned يناير 26, 2022 EST
Build Infrastructure with Terraform on Google Cloud Earned يناير 25, 2022 EST
Set Up an App Dev Environment on Google Cloud Earned يناير 25, 2022 EST
Getting Started with Google Kubernetes Engine Earned يناير 24, 2022 EST
Deploy Kubernetes Applications on Google Cloud Earned يناير 23, 2022 EST
Develop Your Google Cloud Network Earned يناير 20, 2022 EST
Implementing Cloud Load Balancing for Compute Engine Earned يناير 14, 2022 EST
Elastic Google Cloud Infrastructure: Scaling and Automation - Locales Earned يناير 12, 2022 EST
Essential Google Cloud Infrastructure: Core Services Earned يناير 11, 2022 EST
Essential Google Cloud Infrastructure: Foundation Earned يناير 7, 2022 EST
Google Cloud Fundamentals: Core Infrastructure Earned يناير 4, 2022 EST

Demonstrate your ability to implement updated prompt engineering techniques and utilize several of Gemini's key capacilities including multimodal understanding and function calling. Then integrate generative AI into a RAG application deployed to Cloud Run. This course contains labs that are to be used as a test environment. They are deployed to test your understanding as a learner with a limited scope. These technologies can be used with fewer limitations in a real world environment.

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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 course, application developers learn how to design and develop cloud-native applications that seamlessly integrate components from the Google Cloud ecosystem. Through a combination of presentations, demos, and hands-on labs, participants learn how to create repeatable deployments by treating infrastructure as code, choose the appropriate application execution environment for an application, and monitor application performance. Completing one version of each lab is required. Each lab is available in Node.js. In most cases, the same labs are also provided in Python or Java. You may complete each lab in whichever language you prefer.

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Course four of the Anthos series prepares students to consider multiple approaches for modernizing applications and services within Anthos environments. Topics include optimizing workloads on serverless platforms and migrating workloads to Anthos. This course is a continuation of course three, Anthos on Bare Metal, and assumes direct experience with the topics covered in that course.

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In this course, you learn how to design APIs, and how to use OpenAPI specifications to document them. You learn about the API lifecycle, and how the Apigee API platform helps you manage all aspects of the lifecycle. You learn how APIs can be designed using API proxies, and how APIs are packaged as API products to be used by app developers. Through a combination of lessons, hands-on labs, and supplemental materials, you will learn how to design, build, secure, deploy, and manage API solutions using Google Cloud's Apigee API Platform. This is the first course of the Developing APIs with the Apigee API Platform series. After completing this course, enroll in the API Security with the Apigee API Platform course.

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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 content is deprecated. Please see the latest version of the course, here.

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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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(Previously named "Developing apps with Vertex AI Agent Builder: Search". Please note there maybe instances in this course where previous product names and titles are used) Enterprises of all sizes have trouble making their information readily accessible to employees and customers alike. Internal documentation is frequently scattered across wikis, file shares, and databases. Similarly, consumer-facing sites often offer a vast selection of products, services, and information, but customers are frustrated by ineffective site search and navigation capabilities. This course teaches you to use AI Applications to integrate enterprise-grade generative AI search.

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

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

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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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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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Incorporating machine learning into data pipelines increases the ability to extract insights from data. This course covers ways machine learning can be included in data pipelines on Google Cloud. For little to no customization, this course covers AutoML. For more tailored machine learning capabilities, this course introduces Notebooks and BigQuery machine learning (BigQuery ML). Also, this course covers how to productionalize machine learning solutions by using Vertex AI.

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This course is part 1 of a 3-course series on Serverless Data Processing with Dataflow. In this first course, we start with a refresher of what Apache Beam is and its relationship with Dataflow. Next, we talk about the Apache Beam vision and the benefits of the Beam Portability framework. The Beam Portability framework achieves the vision that a developer can use their favorite programming language with their preferred execution backend. We then show you how Dataflow allows you to separate compute and storage while saving money, and how identity, access, and management tools interact with your Dataflow pipelines. Lastly, we look at how to implement the right security model for your use case on Dataflow.

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Earn a skill badge by completing the Measure Site Reliability using Cloud Operations Suite quest, where you will learn how to set Service Level Indicators (SLIs), Service Level Objectives (SLOs), and Service Level Agreements (SLAs); create logs-based metrics to capture to capture specific issues and address them; define alerts to notify Site Reliability Engineers of issues in production environment, and troubleshoot application issues with Cloud Trace, Debugger, Profiler, Monitoring and Logging. 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 this skill badge quest, and the final assessment challenge lab, to receive a skill badge that you can share with your network.

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Welcome to the two-part course on Logging, Monitoring, and Observability in Google Cloud. The core operations tools in Google Cloud break down into two major categories. The operations-focused components and the application performance management tools. This course, Logging and Monitoring in Google Cloud, covers the operations-focused components including Logging, Monitoring, and Service Monitoring. After taking this course, it is suggested that you complete part 2, Observability in Google Cloud, to learn about the available application performance management tools.

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Good news! There’s a new updated version of this learning path available for you!Open the new Professional Cloud DevOps Engineer Certification Learning Path to begin, once you’ve selected the new path all your current progress will be reflected in the new version.

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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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Good news! There’s a new updated version of this learning path available for you!Open the new Professional Cloud DevOps Engineer Certification Learning Path to begin, once you’ve selected the new path all your current progress will be reflected in the new version.

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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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This course discusses the upgrade process for Apigee hybrid, and teaches you how to monitor and troubleshoot the hybrid runtime plane components.

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This course introduces you to the fundamentals and practices used to install and manage Google Cloud's Apigee API Platform for hybrid cloud. Through a combination of lectures, a hands-on lab, and supplemental materials, you will learn how to install and operate the Apigee API Platform.

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Good news! There’s a new updated version of this learning path available for you!Open the new Professional Cloud DevOps Engineer Certification Learning Path to begin, once you’ve selected the new path all your current progress will be reflected in the new version.

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In this intermediate course, you will learn to design, build, and optimize robust batch data pipelines on Google Cloud. Moving beyond fundamental data handling, you will explore large-scale data transformations and efficient workflow orchestration, essential for timely business intelligence and critical reporting. Get hands-on practice using Dataflow for Apache Beam and Serverless for Apache Spark (Dataproc Serverless) for implementation, and tackle crucial considerations for data quality, monitoring, and alerting to ensure pipeline reliability and operational excellence. A basic knowledge of data warehousing, ETL/ELT, SQL, Python, and Google Cloud concepts is recommended.

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While the traditional approaches of using data lakes and data warehouses can be effective, they have shortcomings, particularly in large enterprise environments. This course introduces the concept of a data lakehouse and the Google Cloud products used to create one. A lakehouse architecture uses open-standard data sources and combines the best features of data lakes and data warehouses, which addresses many of their shortcomings.

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This course introduces the Google Cloud big data and machine learning products and services that support the data-to-AI lifecycle. It explores the processes, challenges, and benefits of building a big data pipeline and machine learning models with Vertex AI on Google Cloud.

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Learn how to use Gemini Notebook to create a personalized study guide for the Associate Cloud Engineer certification exam. You'll review Gemini Notebook features, create a notebook in Gemini Notebook , and learn how to use a study guide to practice for a certification exam.

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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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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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Welcome to the Getting Started with Google Kubernetes Engine course. If you're interested in Kubernetes, a software layer that sits between your applications and your hardware infrastructure, then you’re in the right place! Google Kubernetes Engine brings you Kubernetes as a managed service on Google Cloud. The goal of this course is to introduce the basics of Google Kubernetes Engine, or GKE, as it’s commonly referred to, and how to get applications containerized and running in Google Cloud. The course starts with a basic introduction to Google Cloud, and is then followed by an overview of containers and Kubernetes, Kubernetes architecture, and Kubernetes operations.

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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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Earn a skill badge by completing the Develop your Google Cloud Network skill badge course, where you learn multiple ways to deploy and monitor applications including how to: explore IAM roles and add/remove project access, create VPC networks, deploy and monitor Compute Engine VMs, write SQL queries, deploy and monitor VMs in Compute Engine, and deploy applications using Kubernetes with multiple deployment approaches.

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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 course version is for non-English only. If you wish to take this course in English, please enroll here: Elastic Google Cloud Infrastructure: Scaling and Automation. If you wish to take it in another language, change your language in settings to see availability.

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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, systems and applications services. This course also covers deploying practical solutions including customer-supplied encryption keys, security and access management, quotas and billing, and resource monitoring.

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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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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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