vetiver_model() collects the information needed to store, version, and deploy a trained model.
- Create a Plumber API to predict with a deployable
- Write a deployable Plumber file for a vetiver model
- Write a Dockerfile for a vetiver model
- Generate files necessary to build a Docker container for a vetiver model
- Deploy a vetiver model API to RStudio Connect
- Create an RStudio Connect bundle for a vetiver model API
- Create a model API endpoint object for prediction
- Post new data to a deployed model API endpoint and return predictions
- Post new data to a deployed model API endpoint and augment with predictions
Monitor a deployed
vetiver_model() with a dashboard.
- Aggregate model metrics over time for monitoring
- Update model metrics over time for monitoring
- Plot model metrics over time for monitoring
These functions are helpful for developers extending vetiver for other types of models.
- Identify data types for each column in an input data prototype
- Convert new data at prediction time using input data prototype