Detect Manufacturing Defects Using Visual Inspection AI
Earned Aug 1, 2025 EDT
ML-Modelle mit BigQuery ML erstellen
Earned Mai 28, 2025 EDT
Preparing for your Professional Data Engineer Journey
Earned Sep 11, 2023 EDT
Smart Analytics, Machine Learning, and AI on Google Cloud
Earned Dez 28, 2022 EST
Load Balancing in der Compute Engine implementieren
Earned Okt 13, 2022 EDT
Build Streaming Data Pipelines on Google Cloud
Earned Sep 26, 2022 EDT
Build Batch Data Pipelines on Google Cloud
Earned Aug 22, 2022 EDT
Migration Summit 2022
Earned Aug 9, 2022 EDT
Build Data Lakes and Data Warehouses on Google Cloud
Earned Jul 17, 2022 EDT
Google Cloud Big Data and Machine Learning Fundamentals
Earned Jul 5, 2022 EDT
Earn a skill badge by completing the Detect Manufacturing Defects using Visual Inspection AI course, where you learn how to use Visual Inspection AI to deploy a solution artifact and test that it can successfully identify defects in a manufacturing process.
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Mit dem Skill-Logo zum Kurs ML-Modelle mit BigQuery ML erstellen weisen Sie fortgeschrittene Kenntnisse in folgendem Bereich nach: Erstellen und Bewerten von Machine-Learning-Modellen mit BigQuery ML, um Datenvorhersagen zu treffen. Ein Skill-Logo ist ein exklusives digitales Abzeichen, das von Google Cloud ausgestellt wird und Ihre Kenntnisse über unsere Produkte und Dienste belegt. In diesem Zusammenhang wird auch die Fähigkeit bewertet, Ihr Wissen in einer interaktiven praxisnahen Umgebung anzuwenden. Absolvieren Sie eine kursspezifische Aufgabenreihe und die Challenge-Lab-Prüfung, um ein Skill-Logo zu erhalten, das Sie in Ihrem Netzwerk posten können.
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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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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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Mit dem Skill-Logo Load Balancing in der Compute Engine implementieren weisen Sie Kenntnisse in folgenden Bereichen nach: Schreiben von gcloud-Befehlen, Verwenden von Cloud Shell, Erstellen und Bereitstellen von virtuellen Maschinen in der Compute Engine und Konfigurieren von Netzwerk- und HTTP-Load-Balancern. Ein Skill-Logo ist ein exklusives digitales Abzeichen, das von Google Cloud vergeben wird und Ihre Kenntnisse über unsere Produkte und Dienste belegt. In diesem Zusammenhang wird auch die Fähigkeit bewertet, wie Sie Ihr Wissen in einer praxisnahen Geschäftssituation anwenden. Absolvieren Sie eine kursspezifische Aufgabenreihe und die Challenge-Lab-Prüfung, um ein Skill-Logo zu erhalten, das Sie in Ihrem Netzwerk posten können.
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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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The GSI Migration Summit is a curated collection of hands-on labs and quests that provide coverage of application migration. Security and analytics labs and quests have been added to provide coverage of key platform concepts and services.
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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.
Detect Manufacturing Defects Using Visual Inspection AI
Earned Aug 1, 2025 EDT
ML-Modelle mit BigQuery ML erstellen
Earned Mai 28, 2025 EDT
Preparing for your Professional Data Engineer Journey
Earned Sep 11, 2023 EDT
Smart Analytics, Machine Learning, and AI on Google Cloud
Earned Dez 28, 2022 EST
Load Balancing in der Compute Engine implementieren
Earned Okt 13, 2022 EDT
Build Streaming Data Pipelines on Google Cloud
Earned Sep 26, 2022 EDT
Build Batch Data Pipelines on Google Cloud
Earned Aug 22, 2022 EDT
Migration Summit 2022
Earned Aug 9, 2022 EDT
Build Data Lakes and Data Warehouses on Google Cloud
Earned Jul 17, 2022 EDT
Google Cloud Big Data and Machine Learning Fundamentals
Earned Jul 5, 2022 EDT