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sangavi kumar

Member since 2022

Silver League

4740 points
CCAI Academy - Bot Building with Gen AI Earned يناير 29, 2025 EST
Intro to CCAI and CCAI Engagement Framework Earned يناير 29, 2025 EST
Implement Enterprise Design Patterns with CX Agent Studio Earned يناير 28, 2025 EST
[DEPRECATED] Data Engineering Earned مارس 24, 2023 EDT
Build a Certification Study Guide: PDE Exam Prep Earned مارس 18, 2023 EDT
Serverless Data Processing with Dataflow: Foundations Earned مايو 13, 2022 EDT
Smart Analytics, Machine Learning, and AI on Google Cloud Earned مايو 13, 2022 EDT
Build Streaming Data Pipelines on Google Cloud Earned مايو 12, 2022 EDT
Google Cloud Essentials Earned أبريل 6, 2022 EDT
Build Data Lakes and Data Warehouses on Google Cloud Earned مارس 24, 2022 EDT
[DEPRECATED] Google Cloud Big Data and Machine Learning Fundamentals Earned مارس 22, 2022 EDT
Intro to BigQuery: Analytics & Machine Learning Earned مارس 18, 2022 EDT
Cloud Hero Infra II Skills Earned مارس 18, 2022 EDT

The objective of this course is to upsklill experienced conversational AI practitioners on how to deliver Dialogflow Bots with new Gen AI capabilities Brought to you by the GCC Tech Specialization Team (gcc-enablement-tech@). Share your request/feedback on go/learningpacks-feedback!

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This is a introductory course to all solutions in the Contact Centre AI (CCAI) portfolio and the Generative AI features that are poised to transform them. The course also explores the CCAI go to market and engagement model, the business case around CCAI, as well as the use cases and user personas addressed by the solution.

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This course explores using enterprise design patterns to build scalable, reliable, and secure agents within CX Agent Studio. Additionally, it covers navigating the complete development lifecycle by deploying multi-layered application security and locking down cloud infrastructure perimeters. It details the core pillars of multi-turn session persistence and real-time audio supervision to facilitate safe, continuous conversational experiences.

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This advanced-level quest is unique amongst the other catalog offerings. The labs have been curated to give IT professionals hands-on practice with topics and services that appear in the Google Cloud Certified Professional Data Engineer Certification. From Big Query, to Dataprep, to Cloud Composer, this quest is composed of specific labs that will put your Google Cloud data engineering knowledge to the test. Be aware that while practice with these labs will increase your skills and abilities, you will need other preparation, too. The exam is quite challenging and external studying, experience, and/or background in cloud data engineering is recommended. Looking for a hands on challenge lab to demonstrate your skills and validate your knowledge? On completing this quest, enroll in and finish the additional challenge lab at the end of the Engineer Data in the Google Cloud to receive an exclusive Google Cloud digital badge.

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Learn how to use Gemini Notebook to create a personalized study guide for the Professional Data 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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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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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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In this course you will get hands-on in order to work through real-world challenges faced when building streaming data pipelines. The primary focus is on managing continuous, unbounded data with Google Cloud products.

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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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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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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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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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