In this blog post, you will use the second method and train a model directly in Vertex AI since this allows us to automate the model creation process at a later stage while also supporting distributed hyperparameter optimization. Train a model locally and import it as a custom model into Vertex AI Model Registry, from where it can be deployed to an endpoint for serving predictions.Ĭreate a TrainingPipeline that runs a CustomJob and imports the resulting artifacts as a Model.

Models on Vertex AI can be created in two ways: For R, you build a container yourself, derived from Google Cloud Deep Learning Containers for R. Vertex AI provides pre-built Docker containers for model training and serving predictions for models written in tensorflow, scikit-learn and xgboost. Since many R users prefer to interact with Vertex AI from RStudio programmatically, you will interact with Vertex AI through the Vertex AI SDK via the reticulate package. Managing machine learning models on Vertex AI can be done in a variety of ways, including using the User Interface of the Google Cloud Console, API calls, or the Vertex AI SDK for Python. In this blog post, you will walk through how to use Google Vertex AI to train and deploy enterprise-grade machine learning models built with R.

Once a model has been built successfully, a recurring question among data scientists is: 'How do I deploy models written in the R language to production in a scalable, reliable and low-maintenance way?' RStudio, available as desktop version or on the Google Cloud Marketplace, is a popular Integrated Development Environment (IDE) used by data professionals for visualization and machine learning model development. Besides the tidyverse, there are over 18,000 open-source packages on CRAN, the package repository for R. Many data scientists love it, especially for the rich world of packages from tidyverse, an opinionated collection of R packages for data science.

R is one of the most widely used programming languages for statistical computing and machine learning.

Google cloud rstudio