CRAN Task View: Model Deployment with R

Maintainer:Yuan Tang, James Joseph Balamuta
Contact:terrytangyuan at
Contributions:Suggestions and improvements for this task view are very welcome and can be made through issues or pull requests on GitHub or via e-mail to the maintainer address. For further details see the Contributing guide.
Citation:Yuan Tang, James Joseph Balamuta (2022). CRAN Task View: Model Deployment with R. Version 2022-08-24. URL
Installation:The packages from this task view can be installed automatically using the ctv package. For example, ctv::install.views("ModelDeployment", coreOnly = TRUE) installs all the core packages or ctv::update.views("ModelDeployment") installs all packages that are not yet installed and up-to-date. See the CRAN Task View Initiative for more details.

This CRAN task view contains a list of packages, grouped by topic, that provides functionalities to streamline the process of deploying models to various environments, such as mobile devices, edge devices, cloud, and GPUs, for scoring or inferencing on new data. It complements the related task views on HighPerformanceComputing and MachineLearning.

Model deployment is often challenging due to various reasons. Some example challenges are:

Many of the areas discussed in this task view are undergoing rapid changes in industries and academia. Please send any suggestions to the maintainer via e-mail or submit an issue or pull request in the GitHub repository linked above. All suggestions and corrections by others are gratefully acknowledged.

Deployment through different types of artifacts

This section includes packages that provides functionalities to export the trained model to an artifact that could fit in small devices such as mobile devices (e.g. Android, iOS) and edge devices (Rasberri Pi). These packages are built based on different model format.

Deployment through cloud/server

Many deployment environments are based on cloud/server. The following packages provides functionalities to deploy models in those types of environments:

CRAN packages

Regular:arules, arulesCBA, arulesSequences, cloudml, dbplyr, domino, dplyr, FastRWeb, h2o, httpuv, ibmdbR, keras, lightgbm, onnx, opencpu, plumber, pmml, pmmlTransformations, RestRserve, reticulate, RSclient, Rserve, rsparkling, sparklyr, tensorflow, tfdeploy, tfestimators, tidypredict, vetiver, xgboost, yhatr.

Related links

Other resources