This course is designed for data analysts who want to learn about using BigQuery for their data analysis needs. Through a combination of videos, labs, and demos, we cover various topics that discuss how to ingest, transform, and query your data in BigQuery to derive insights that can help in business decision making.
Weitere Informationen
This course explores the benefits of using Vertex AI Feature Store, how to improve the accuracy of ML models, and how to find which data columns make the most useful features. This course also includes content and labs on feature engineering using BigQuery ML, Keras, and TensorFlow.
Weitere Informationen
This course covers building ML models with TensorFlow and Keras, improving the accuracy of ML models and writing ML models for scaled use.
Weitere Informationen
Mit dem Skill-Logo zum Kurs ML-Lösungen mit Vertex AI erstellen und bereitstellen weisen Sie fortgeschrittene Kenntnisse nach. Sie lernen in diesem Kurs, wie Sie die Vertex AI-Plattform von Google Cloud, AutoML und benutzerdefinierte Trainingsdienste nutzen, um Machine-Learning-Modelle zu trainieren, zu bewerten, abzustimmen, zu erklären und bereitzustellen. Dieser Kurs richtet sich an professionelle Data Scientists und Machine Learning Engineers. Ein Skill-Logo ist ein exklusives digitales Abzeichen, das von Google Cloud ausgestellt wird und Ihre Kenntnisse über Produkte und Dienste von Google Cloud belegt. In diesem Zusammenhang wird auch die Fähigkeit bewertet, Ihr Wissen in einer interaktiven praxisnahen Umgebung anzuwenden. Absolvieren Sie diese Aufgabenreihe und die Challenge-Lab-Prüfung, um ein digitales Abzeichen zu erhalten, das Sie in Ihrem Netzwerk posten können.
Weitere Informationen
This course covers how to implement the various flavors of production ML systems— static, dynamic, and continuous training; static and dynamic inference; and batch and online processing. You delve into TensorFlow abstraction levels, the various options for doing distributed training, and how to write distributed training models with custom estimators.
This is the second course of the Advanced Machine Learning on Google Cloud series. After completing this course, enroll in the Image Understanding with TensorFlow on Google Cloud course.
Weitere Informationen
This course introduces participants to MLOps tools and best practices for deploying, evaluating, monitoring and operating production ML systems on Google Cloud. MLOps is a discipline focused on the deployment, testing, monitoring, and automation of ML systems in production.
Learners will get hands-on practice using Agent Platform Feature Store's streaming ingestion at the SDK layer.
Weitere Informationen
This course introduces participants to MLOps tools and best practices for deploying, evaluating, monitoring and operating production ML systems on Google Cloud. MLOps is a discipline focused on the deployment, testing, monitoring, and automation of ML systems in production. Machine Learning Engineering professionals use tools for continuous improvement and evaluation of deployed models. They work with (or can be) Data Scientists, who develop models, to enable velocity and rigor in deploying the best performing models.
Weitere Informationen
This course takes a real-world approach to the ML Workflow through a case study. An ML team faces several ML business requirements and use cases. The team must understand the tools required for data management and governance and consider the best approach for data preprocessing.
The team is presented with three options to build ML models for two use cases. The course explains why they would use AutoML, BigQuery ML, or custom training to achieve their objectives.
Weitere Informationen
BigQuery for Data Analysts
Earned Feb 20, 2024 EST
[DEPRECATED] Feature Engineering
Earned Feb 12, 2024 EST
[Depricated] Build, Train and Deploy ML Models with Keras on Google Cloud
Earned Feb 7, 2024 EST
ML-Lösungen mit Vertex AI erstellen und bereitstellen
Earned Jan 29, 2024 EST
Production Machine Learning Systems
Earned Jan 19, 2024 EST
MLOps with Agent Platform: Manage Features
Earned Jan 17, 2024 EST
Machine Learning Operations (MLOps): Getting Started
Earned Jan 17, 2024 EST
Machine Learning in the Enterprise
Earned Mai 24, 2023 EDT