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Qi Rong Lai

Member since 2022

Machine Learning Operations (MLOps) with Vertex AI: Manage Features Earned سبتمبر 9, 2024 EDT
Machine Learning Operations (MLOps): Getting Started Earned سبتمبر 4, 2024 EDT
Feature Engineering Earned أغسطس 24, 2024 EDT
Natural Language Processing on Google Cloud Earned أغسطس 19, 2024 EDT
Launching into Machine Learning Earned أغسطس 4, 2024 EDT
Google Cloud Big Data and Machine Learning Fundamentals Earned أغسطس 4, 2024 EDT
Recommendation Systems on Google Cloud Earned أغسطس 3, 2024 EDT
Prepare Data for ML APIs on Google Cloud Earned يوليو 26, 2024 EDT
Build, Train and Deploy ML Models with Keras on Google Cloud Earned يوليو 24, 2024 EDT
Build and Deploy Machine Learning Solutions on Vertex AI Earned يوليو 22, 2024 EDT
Computer Vision Fundamentals with Google Cloud Earned يوليو 21, 2024 EDT
Reliable Google Cloud Infrastructure: Design and Process Earned يونيو 14, 2024 EDT
Optimize Costs for Google Kubernetes Engine Earned يونيو 11, 2024 EDT
Developing a Google SRE Culture Earned يونيو 4, 2024 EDT
Preparing for your Professional Cloud Architect Journey Earned مايو 31, 2024 EDT
Gemini for Network Engineers Earned مايو 25, 2024 EDT
Gemini for Data Scientists and Analysts Earned مايو 3, 2024 EDT
Service Orchestration and Choreography on Google Cloud Earned أبريل 12, 2024 EDT
Introduction to Responsible AI Earned يناير 28, 2024 EST
Introduction to Large Language Models Earned يناير 22, 2024 EST
Introduction to Generative AI Earned يناير 22, 2024 EST
Gemini for Cloud Architects Earned يناير 17, 2024 EST
Gemini for Application Developers Earned يناير 16, 2024 EST
Logging and Monitoring in Google Cloud Earned يناير 16, 2024 EST
Gemini in Google Slides Earned يناير 13, 2024 EST
Gemini in Google Sheets Earned يناير 9, 2024 EST
Gemini in Google Docs Earned يناير 9, 2024 EST
Gemini in Gmail Earned يناير 9, 2024 EST
Introduction to Gemini for Google Workspace Earned يناير 8, 2024 EST
Networking in Google Cloud: Routing and Addressing Earned يناير 8, 2024 EST
Automate Deployment and Manage Traffic on a Google Cloud Network Earned يناير 6, 2024 EST
Networking in Google Cloud: Fundamentals Earned يناير 5, 2024 EST
Google Cloud Fundamentals: Core Infrastructure Earned يناير 2, 2024 EST
Google Kubernetes Engine Best Practices: Security Earned ديسمبر 17, 2023 EST
Generative AI Explorer Earned ديسمبر 13, 2023 EST
Security & Identity Fundamentals Earned مارس 31, 2023 EDT
Application Development I Earned سبتمبر 8, 2022 EDT
Intro to Infrastructure Earned سبتمبر 6, 2022 EDT

This course introduces participants to MLOps tools and best practices for deploying, evaluating, monitoring and operating production ML systems on Google Cloud. MLOps is a discipline focused on the deployment, testing, monitoring, and automation of ML systems in production. Learners will get hands-on practice using Vertex AI Feature Store's streaming ingestion at the SDK layer.

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This course introduces participants to MLOps tools and best practices for deploying, evaluating, monitoring and operating production ML systems on Google Cloud. MLOps is a discipline focused on the deployment, testing, monitoring, and automation of ML systems in production. Machine Learning Engineering professionals use tools for continuous improvement and evaluation of deployed models. They work with (or can be) Data Scientists, who develop models, to enable velocity and rigor in deploying the best performing models.

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This course explores the benefits of using Vertex AI Feature Store, how to improve the accuracy of ML models, and how to find which data columns make the most useful features. This course also includes content and labs on feature engineering using BigQuery ML, Keras, and TensorFlow.

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This course introduces the products and solutions to solve NLP problems on Google Cloud. Additionally, it explores the processes, techniques, and tools to develop an NLP project with neural networks by using Vertex AI and TensorFlow.

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The course begins with a discussion about data: how to improve data quality and perform exploratory data analysis. We describe Vertex AI AutoML and how to build, train, and deploy an ML model without writing a single line of code. You will understand the benefits of Big Query ML. We then discuss how to optimize a machine learning (ML) model and how generalization and sampling can help assess the quality of ML models for custom training.

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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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In this course, you apply your knowledge of classification models and embeddings to build a ML pipeline that functions as a recommendation engine. This is the fifth and final course of the Advanced Machine Learning on Google Cloud series.

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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 Dataproc, and calling ML APIs including the Cloud Natural Language API, Google Cloud Speech-to-Text API, and Video Intelligence API. 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 course, and the final assessment challenge lab, to receive a skill badge that you can share with your network.

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This course covers building ML models with TensorFlow and Keras, improving the accuracy of ML models and writing ML models for scaled use.

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Earn the intermediate skill badge by completing the Build and Deploy Machine Learning Solutions on Vertex AI course, where you will 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. This skill badge course is for professional Data Scientists and Machine Learning Engineers. 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, and the final assessment challenge lab, to receive a digital badge that you can share with your network.

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This course describes different types of computer vision use cases and then highlights different machine learning strategies for solving these use cases. The strategies vary from experimenting with pre-built ML models through pre-built ML APIs and AutoML Vision to building custom image classifiers using linear models, deep neural network (DNN) models or convolutional neural network (CNN) models. The course shows how to improve a model's accuracy with augmentation, feature extraction, and fine-tuning hyperparameters while trying to avoid overfitting the data. The course also looks at practical issues that arise, for example, when one doesn't have enough data and how to incorporate the latest research findings into different models. Learners will get hands-on practice building and optimizing their own image classification models on a variety of public datasets in the labs they will work on.

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This course equips students to build highly reliable and efficient solutions on Google Cloud using proven design patterns. It is a continuation of the Architecting with Google Compute Engine or Architecting with Google Kubernetes Engine courses and assumes hands-on experience with the technologies covered in either of those courses. Through a combination of presentations, design activities, and hands-on labs, participants learn to define and balance business and technical requirements to design Google Cloud deployments that are highly reliable, highly available, secure, and cost-effective.

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Complete the intermediate Optimize Costs for Google Kubernetes Engine skill badge to demonstrate skills in the following: creating and managing multi-tenant clusters, monitoring resource usage by namespace, configuring cluster and pod autoscaling for efficiency, setting up load balancing for optimal resource distribution, and implementing liveness and readiness probes to ensure application health and cost-effectiveness. 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 course and the final assessment challenge lab to receive a skill badge that you can share with your network.

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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 helps learners create a study plan for the PCA (Professional Cloud Architect) certification exam. Learners explore the breadth and scope of the domains covered in the exam. Learners assess their exam readiness and create their individual study plan.

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In this course, you learn how Gemini, a generative AI-powered collaborator from Google Cloud, helps network engineers create, update, and maintain VPC networks. You learn how to prompt Gemini to provide specific guidance for your networking tasks, beyond what you would receive from a search engine. Using a hands-on lab, you experience how Gemini makes it easier for you to work with Google Cloud VPC networks. Duet AI was renamed to Gemini, our next-generation model.

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In this course, you learn how Gemini, a generative AI-powered collaborator from Google Cloud, helps analyze customer data and predict product sales. You also learn how to identify, categorize, and develop new customers using customer data in BigQuery. Using hands-on labs, you experience how Gemini improves data analysis and machine learning workflows. Duet AI was renamed to Gemini, our next-generation model.

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This course introduces you to event-based applications and teaches you how to use service orchestration and choreography to coordinate microservices. Using lectures and hands-on labs, you learn how to use Workflows, Eventarc, Cloud Tasks, and Cloud Scheduler to build microservices applications on Google Cloud.

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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 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.

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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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In this course, you learn how Gemini, a generative AI-powered collaborator from Google Cloud, helps administrators provision infrastructure. You learn how to prompt Gemini to explain infrastructure, deploy GKE clusters and update existing infrastructure. Using a hands-on lab, you experience how Gemini improves the GKE deployment workflow. Duet AI was renamed to Gemini, our next-generation model.

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In this course, you learn how Gemini, a generative AI-powered collaborator from Google Cloud, helps developers build applications. You learn how to prompt Gemini to explain code, recommend Google Cloud services, and generate code for your applications. Using a hands-on lab, you experience how Gemini improves the application development workflow. Duet AI was renamed to Gemini, our next-generation model.

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This course teaches participants techniques for monitoring and improving infrastructure and application performance in Google Cloud. Using a combination of presentations, demos, hands-on labs, and real-world case studies, attendees gain experience with full-stack monitoring, real-time log management and analysis, debugging code in production, tracing application performance bottlenecks, and profiling CPU and memory usage.

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Gemini for Google Workspace provides customers with generative AI features in Google Workspace. In this mini-course, you learn about the key features of Gemini and how they can be used to improve productivity and efficiency in Google Slides.

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Gemini for Google Workspace provides customers with generative AI features in Google Workspace. In this mini-course, you learn about the key features of Gemini and how they can be used to improve productivity and efficiency in Google Sheets.

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Gemini for Google Workspace provides customers with access to generative AI features. This course delves into the capabilities of Gemini in Google Docs using video lessons, hands-on activities and practical examples. You learn how to use Gemini to generate written content based on prompts. You also explore using Gemini to edit text you’ve already written, helping you improve your overall productivity. By the end of this course, you'll be equipped with the knowledge and skills to confidently utilize Gemini in Google Docs to improve your writing.

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Gemini for Google Workspace provides customers with generative AI features in Google Workspace. In this mini-course, you learn about the key features of Gemini and how they can be used to improve productivity and efficiency in Gmail.

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Gemini for Google Workspace provides customers with generative AI features in Google Workspace. In this learning path, you learn about the key features of Gemini and how they can be used to improve productivity and efficiency in Google Workspace.

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Welcome to the second course in the networking and Google Cloud series routing and addressing. In this course, we'll cover the central routing and addressing concepts that are relevant to Google Cloud's networking capabilities. Module one will lay the foundation by exploring network routing and addressing in Google Cloud, covering key building blocks such as routing IPv4, bringing your own IP addresses and setting up cloud DNS. In Module two will shift our focus to private connection options, exploring use cases and methods for accessing Google and other services privately using internal IP addresses. By the end of this course, you'll have a solid grasp of how to effectively route and address your network traffic within Google Cloud.

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

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Networking in Google cloud is a 6 part course series. Welcome to the first course of our six part course series, Networking in Google Cloud: Fundamentals.  This course provides a comprehensive overview of core networking concepts, including networking fundamentals, virtual private clouds (VPCs), and the sharing of VPC networks. Additionally, the course covers network logging and monitoring techniques. 

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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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Get Anthos Ready. This Google Kubernetes Engine-centric quest of best practice hands-on labs focuses on security at scale when deploying and managing production GKE environments -- specifically role-based access control, hardening, VPC networking, and binary authorization.

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Welcome Gamers! Create and test a prompt, create a conversation, and explore the prompt gallery, all while having fun! Explore Generative AI Studio in Vertex AI. You will compete to see who can finish the game with the highest score. Earn the points by completing the steps in the lab.... and get bonus points for speed! Be sure to click "End" when you're done with each lab to get the maximum points. All players will be awarded the game badge.

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Security is an uncompromising feature of Google Cloud services, and Google Cloud has developed specific tools for ensuring safety and identity across your projects. In this fundamental-level quest, you will get hands-on practice with Google Cloud’s Identity and Access Management (IAM) service, which is the go-to for managing user and virtual machine accounts. You will get experience with network security by provisioning VPCs and VPNs, and learn what tools are available for security threat and data loss protections.

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Have fun with this interesting game and get some hands on Stackdriver, Docker and Python. Challenge yourself to complete each task as quickly and accurately as possible to score points and earn badges!

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Welcome Gamers! You will compete to see who can finish the game with the highest score. Earn the points by completing the steps in the lab.... and get bonus points for speed! Be sure to click "End" when you're done with each lab to get the maximum points. All players will be awarded the game badge.

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