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在 Google Cloud 控制台中运用您的技能

Aldo Galán Méndez

成为会员时间：2023

面向数据科学家和分析师的 Gemini Earned Aug 14, 2025 EDT
Google Cloud 上的 AI 和机器学习简介 Earned Jun 14, 2024 EDT
Modernizing Data Lakes and Data Warehouses with Google Cloud Earned Oct 27, 2023 EDT
Preparing for your Professional Data Engineer Journey Earned Oct 4, 2023 EDT
Launching into Machine Learning Earned Jun 23, 2023 EDT
How Google Does Machine Learning Earned May 23, 2023 EDT
Google Cloud Big Data and Machine Learning Fundamentals Earned May 19, 2023 EDT

在本课程中，您将了解 Gemini（Google Cloud 的生成式 AI 赋能的协作工具）如何帮助分析客户数据并预测产品销售情况。此外，您还将了解如何在 BigQuery 中使用客户数据来识别、开发新客户并对其进行分类。通过动手实验，您将体验 Gemini 如何改进数据分析和机器学习工作流。 Duet AI 已更名为 Gemini，这是我们的新一代模型。

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本课程介绍 Google Cloud 中的 AI 和机器学习 (ML) 服务，这些服务可构建预测式和生成式 AI 项目。本课程探讨从数据到 AI 的整个生命周期中可用的技术、产品和工具，包括 AI 基础、开发和解决方案。通过引人入胜的学习体验和实操练习，本课程可帮助数据科学家、AI 开发者和机器学习工程师提升技能和知识水平。

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The two key components of any data pipeline are data lakes and warehouses. This course highlights use-cases for each type of storage and dives into the available data lake and warehouse solutions on Google Cloud in technical detail. Also, this course describes the role of a data engineer, the benefits of a successful data pipeline to business operations, and examines why data engineering should be done in a cloud environment. This is the first course of the Data Engineering on Google Cloud series. After completing this course, enroll in the Building Batch Data Pipelines on Google Cloud course.

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This course helps learners create a study plan for the PDE (Professional Data Engineer) 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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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 explores what ML is and what problems it can solve. The course also discusses best practices for implementing machine learning. You’re introduced to Vertex AI, a unified platform to quickly build, train, and deploy AutoML machine learning models. The course discusses the five phases of converting a candidate use case to be driven by machine learning, and why it’s important to not skip them. The course ends with recognizing the biases that ML can amplify and how to recognize them.

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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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