We are currently experiencing intermittent issues with the Gemini Enterprise labs. Please bear with us while we work on the resolution.
Welcome to Cloud Composer, where we discuss how to orchestrate data lake workflows with Cloud Composer.
En savoir plus
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.
En savoir plus
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.
En savoir plus
This course introduces Google Cloud's AI and machine learning (ML) capabilities, with a focus on developing both generative and predictive AI projects. It explores the various technologies, products, and tools available throughout the data-to-AI lifecycle, empowering data scientists, AI developers, and ML engineers to enhance their expertise through interactive exercises.
En savoir plus
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.
En savoir plus
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.
En savoir plus
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.
En savoir plus
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.
En savoir plus
Data Lake Modernization on Google Cloud: Cloud Composer
Earned août 4, 2025 EDT
[DEPRECATED] Gemini for Data Scientists and Analysts
Earned juil. 29, 2025 EDT
Launching into Machine Learning
Earned juin 12, 2024 EDT
Introduction to AI and Machine Learning on Google Cloud
Earned avr. 30, 2024 EDT
Build a Certification Study Guide: PDE Exam Prep
Earned oct. 23, 2023 EDT
Build Data Lakes and Data Warehouses on Google Cloud
Earned mai 26, 2023 EDT
Serverless Data Processing with Dataflow: Foundations
Earned mai 24, 2023 EDT
[DEPRECATED] Google Cloud Big Data and Machine Learning Fundamentals
Earned mai 17, 2023 EDT