К содержимому
learnspaceYOUR NEXT CHAPTER
ПРОСТРАНСТВО ОБУЧЕНИЯ
ГлавнаяКаталог курсовМоё обучениеCoursera

Знания без границ

Учитесь у лучших университетов и компаний мира.

Открыть Coursera
Интеграция
Пространство университета
Моё пространствоСтраница курса
↵
ЯЛичный кабинетСтудент
© 2026 LearnSpaceКаждый день — возможность узнать больше.Помощь
MLOps in R: Deploying machine learning models using vetiver · LearnSpace
Назад в каталог
courseraАнализ данных

MLOps in R: Deploying machine learning models using vetiver

Курс от Coursera
Средний≈ 2.7 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

Did you know that over 70% of machine learning models never make it into production? Are you ready to defy the odds and become a master at deploying machine learning models in R? This Guided Project was created to help data professionals accomplish efficient model deployment using Vetiver in R. More specifically, in this 2-hour long project-based course, you will learn how to build an ensemble model, set up the deployment framework, deploy the model using various methods, and monitor model performance. To achieve this, you will create a fully automated deployment pipeline by working through a realistic scenario of deploying a hospital readmission model in a healthcare setting. This project is unique because it combines hands-on experience with the aim to bridge the gap between machine learning development and production deployment. In order to be successful in this project, you will need a solid understanding of R programming, basic machine learning concepts, and familiarity with building machine learning models using tidymodels.

Навыки, которые вы освоите

MLOps (Machine Learning Operations)R ProgrammingModel DeploymentContinuous DeploymentPredictive ModelingApplication Programming Interface (API)Model EvaluationContainerizationContinuous MonitoringDocker (Software)Dashboard CreationHealth InformaticsMachine Learning MethodsModel TrainingTidyverse (R Package)Applied Machine Learning

Программа курса

1 модулей · 24 учебных материалов

01Project Overview24 материалов

Your Learning Journey

Project OverviewЧтениеAccess your Project FilesЧтениеTask 1: Set up and overview of the projectВидеоTask 2: Load the model's componentsВидео

Учитесь у экспертов

Arimoro Olayinka Imisioluwa

Guided Project Instructor

MLOps in R: Deploying machine learning models using vetiver
В каталоге вашей программы

Инвестируйте в себя

Новые знания — в удобное для вас время.

Начать на Coursera

Обучение откроется на Coursera
в новой вкладке

Обучение на Coursera

≈ 2.7 ч

1 модулей

Язык: Английский

Субтитры: Арабский, Французский, Итальянский, Бразильский португальский, Корейский, Немецкий, Испанский, Японский, Венгерский

Часть программы вашего университета
Task 3: Create a stacked modelВидео
Task 4: Evaluate the model on the test setВидео
Practice Activity One: Build a stacked modelВидео
Task 5: Create a vetiver objectВидео
Task 6: Store and version the modelВидео
Task 7: Create a REST API for deploymentВидео
Practice Activity Two: Deploy a stroke prediction modelВидео
Task 8: Create a pins board for deploymentВидео
Task 9: Deploy the model via DockerВидео
Task 10: Predict from the model's endpointВидео
Practice Activity Three: Make predictions from the model's endpointВидео
Task 11: Compute monitoring metricsВидео
Task 12: Pin monitoring metricsВидео
Task 13: Plot monitoring metricsВидео
Task 14: Build a monitoring dashboardВидео
Cumulative Activity: Deploy a stroke prediction modelВидео
MLOps in R: Deploying Machine Learning Models using VetiverЗадание
Link to additional project resourcesЧтение
Key TakeawaysЧтение
Course End Survey - We appreciate your feedback!PLUGIN