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Mettez en pratique vos compétences dans la console Google Cloud

George Balan

Date d'abonnement : 2020

Ligue d'Argent

13444 points
Responsible AI: Applying AI Principles with Google Cloud Earned oct. 8, 2025 EDT
Data Warehousing for Partners: Process Data with Dataflow Earned juin 15, 2025 EDT
Data Warehousing for Partners: Analyze Data with Looker Earned juin 15, 2025 EDT
Data Warehousing for Partners: Process Data with Dataproc Earned juin 15, 2025 EDT
Data Warehousing for Partners: Optimize in BigQuery Earned juin 12, 2025 EDT
Data Warehousing for Partners: Stream Data with Pub/Sub Earned juin 12, 2025 EDT
Data Warehousing for Partners: Cloud Data Fusion Pipelines Earned juin 12, 2025 EDT
Data Warehousing for Partners: Migrate Data to BigQuery Earned juin 11, 2025 EDT
Data Warehousing for Partners: Design in BigQuery Earned juin 10, 2025 EDT
Data Warehousing for Partners: Enable Google Cloud Customers Earned avr. 14, 2025 EDT
Introduction to Responsible AI Earned nov. 27, 2023 EST
Generative AI Fundamentals Earned nov. 24, 2023 EST
Introduction to Large Language Models Earned nov. 24, 2023 EST
Introduction to Generative AI Earned nov. 22, 2023 EST
Build a Data Warehouse with BigQuery Earned août 23, 2023 EDT
Prepare Data for ML APIs on Google Cloud Earned juil. 14, 2023 EDT
Serverless Data Processing with Dataflow: Foundations Earned juin 23, 2023 EDT
Smart Analytics, Machine Learning, and AI on Google Cloud Earned juin 20, 2023 EDT
Build Streaming Data Pipelines on Google Cloud Earned juin 15, 2023 EDT
Build Batch Data Pipelines on Google Cloud Earned juin 11, 2023 EDT
Build Data Lakes and Data Warehouses on Google Cloud Earned juin 3, 2023 EDT
Intro to BigQuery: Analytics & Machine Learning Earned déc. 14, 2021 EST
BigQuery for Data Analysis I Earned déc. 8, 2021 EST

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 continues to explore the implementation of data load and transformation pipelines for a BigQuery Data Warehouse using Dataflow.

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This course explores how to leverage Looker to create data experiences and gain insights with modern business intelligence (BI) and reporting.

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This course explores the implementation of data load and transformation pipelines for a BigQuery Data Warehouse using Dataproc.

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Welcome to Optimize in BigQuery, where we map Enterprise Data Warehouse concepts and components to BigQuery and Google data services with a focus on optimization.

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This course explores how to implement a streaming analytics solution using Pub/Sub.

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This course continues to explore the implementation of data load and transformation pipelines for a BigQuery Data Warehouse using Cloud Data Fusion.

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This course identifies best practices for migrating data warehouses to BigQuery and the key skills required to perform successful migration.

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Welcome to Design in BigQuery, where we map Enterprise Data Warehouse concepts and components to BigQuery and Google data services with a focus on schema design.

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

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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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Earn a skill badge by completing the Introduction to Generative AI, Introduction to Large Language Models and Introduction to Responsible AI courses. 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 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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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 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.

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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 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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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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Welcome Gamers! Learn BigQuery and Cloud SQL, all while having fun! 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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