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Emmanuel Rosas

Mitglied seit 2023

Data Lake Modernization on Google Cloud: Cloud Composer Earned Aug 4, 2025 EDT
[DEPRECATED] Gemini für Data Scientists und Analysts Earned Jul 29, 2025 EDT
Launching into Machine Learning Earned Jun 12, 2024 EDT
Einführung in KI und maschinelles Lernen in Google Cloud Earned Apr 30, 2024 EDT
Build a Certification Study Guide: PDE Exam Prep Earned Okt 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

Welcome to Cloud Composer, where we discuss how to orchestrate data lake workflows with Cloud Composer.

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In diesem Kurs erfahren Sie, wie Sie Gemini, ein auf generativer KI basierendes Produkt von Google Cloud, bei der Analyse von Kundendaten und der Prognose von Produktverkäufen unterstützen kann. Außerdem lernen Sie, wie Sie mithilfe von Kundendaten in BigQuery Neukunden identifizieren, kategorisieren und gewinnen können. In den praxisorientierten Labs erfahren Sie, wie Gemini Datenanalysen und Workflows für Machine Learning optimiert. Duet AI wurde umbenannt in Gemini, unser Modell der nächsten Generation.

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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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In diesem Kurs lernen Sie die KI- und ML-Funktionen von Google Cloud kennen. Der Schwerpunkt liegt dabei auf der Entwicklung von Projekten mit generativer und prädiktiver KI. Dabei werden die verschiedenen Technologien, Produkte und Tools vorgestellt, die während des gesamten Data-to-AI-Lebenszyklus verfügbar sind. Data Scientists, Fachkräfte für KI-Entwicklung und ML-Engineers können ihr Fachwissen durch interaktive Übungen erweitern.

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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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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 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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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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