# Version, Share, Deploy, and Monitor Models

## Documentation for package ‘vetiver’ version 0.2.4

- DESCRIPTION file.
- User guides, package vignettes and other documentation.
- Package NEWS.

## Help Pages

A G H L M N P V

## -- A --

api_spec | Update the OpenAPI specification using model metadata
attach_pkgs | Fully attach or load packages for making model predictions
augment.vetiver_endpoint | Post new data to a deployed model API endpoint and augment with predictions
augment.vetiver_endpoint_sagemaker | Post new data to a deployed SageMaker model endpoint and augment with predictions

## -- G --

get_vetiver_dashboard_pins | R Markdown format for model monitoring dashboards
glue_spec_summary | Update the OpenAPI specification using model metadata
glue_spec_summary.array | Update the OpenAPI specification using model metadata
glue_spec_summary.data.frame | Update the OpenAPI specification using model metadata
glue_spec_summary.default | Update the OpenAPI specification using model metadata

## -- H --

handler_predict | Model handler functions for API endpoint
handler_predict.default | Model handler functions for API endpoint
handler_predict.gam | Model handler functions for API endpoint
handler_predict.glm | Model handler functions for API endpoint
handler_predict.keras.engine.training.Model | Model handler functions for API endpoint
handler_predict.kproto | Model handler functions for API endpoint
handler_predict.Learner | Model handler functions for API endpoint
handler_predict.lm | Model handler functions for API endpoint
handler_predict.luz_module_fitted | Model handler functions for API endpoint
handler_predict.model_stack | Model handler functions for API endpoint
handler_predict.ranger | Model handler functions for API endpoint
handler_predict.recipe | Model handler functions for API endpoint
handler_predict.train | Model handler functions for API endpoint
handler_predict.workflow | Model handler functions for API endpoint
handler_predict.xgb.Booster | Model handler functions for API endpoint
handler_startup | Model handler functions for API endpoint
handler_startup.default | Model handler functions for API endpoint
handler_startup.gam | Model handler functions for API endpoint
handler_startup.keras.engine.training.Model | Model handler functions for API endpoint
handler_startup.Learner | Model handler functions for API endpoint
handler_startup.luz_module_fitted | Model handler functions for API endpoint
handler_startup.model_stack | Model handler functions for API endpoint
handler_startup.ranger | Model handler functions for API endpoint
handler_startup.recipe | Model handler functions for API endpoint
handler_startup.train | Model handler functions for API endpoint
handler_startup.workflow | Model handler functions for API endpoint
handler_startup.xgb.Booster | Model handler functions for API endpoint

## -- L --

load_pkgs | Fully attach or load packages for making model predictions

## -- M --

map_request_body | Identify data types for each column in an input data prototype

## -- N --

new_vetiver_model | Create a vetiver object for deployment of a trained model

## -- P --

pin_example_kc_housing_model | R Markdown format for model monitoring dashboards
predict.vetiver_endpoint | Post new data to a deployed model API endpoint and return predictions
predict.vetiver_endpoint_sagemaker | Post new data to a deployed SageMaker model endpoint and return predictions

## -- V --

vetiver_api | Create a Plumber API to predict with a deployable 'vetiver_model()' object
vetiver_compute_metrics | Aggregate model metrics over time for monitoring
vetiver_create_description | Model constructor methods
vetiver_create_description.default | Model constructor methods
vetiver_create_description.gam | Model constructor methods
vetiver_create_description.glm | Model constructor methods
vetiver_create_description.keras.engine.training.Model | Model constructor methods
vetiver_create_description.kproto | Model constructor methods
vetiver_create_description.Learner | Model constructor methods
vetiver_create_description.lm | Model constructor methods
vetiver_create_description.luz_module_fitted | Model constructor methods
vetiver_create_description.model_stack | Model constructor methods
vetiver_create_description.ranger | Model constructor methods
vetiver_create_description.recipe | Model constructor methods
vetiver_create_description.train | Model constructor methods
vetiver_create_description.workflow | Model constructor methods
vetiver_create_description.xgb.Booster | Model constructor methods
vetiver_create_meta | Metadata constructors for 'vetiver_model()' object
vetiver_create_meta.default | Metadata constructors for 'vetiver_model()' object
vetiver_create_meta.gam | Metadata constructors for 'vetiver_model()' object
vetiver_create_meta.keras.engine.training.Model | Metadata constructors for 'vetiver_model()' object
vetiver_create_meta.kproto | Metadata constructors for 'vetiver_model()' object
vetiver_create_meta.Learner | Metadata constructors for 'vetiver_model()' object
vetiver_create_meta.luz_module_fitted | Metadata constructors for 'vetiver_model()' object
vetiver_create_meta.model_stack | Metadata constructors for 'vetiver_model()' object
vetiver_create_meta.ranger | Metadata constructors for 'vetiver_model()' object
vetiver_create_meta.recipe | Metadata constructors for 'vetiver_model()' object
vetiver_create_meta.train | Metadata constructors for 'vetiver_model()' object
vetiver_create_meta.workflow | Metadata constructors for 'vetiver_model()' object
vetiver_create_meta.xgb.Booster | Metadata constructors for 'vetiver_model()' object
vetiver_create_ptype | Create a vetiver input data prototype
vetiver_create_rsconnect_bundle | Create an Posit Connect bundle for a vetiver model API
vetiver_dashboard | R Markdown format for model monitoring dashboards
vetiver_deploy_rsconnect | Deploy a vetiver model API to Posit Connect
vetiver_deploy_sagemaker | Deploy a vetiver model API to Amazon SageMaker
vetiver_endpoint | Create a model API endpoint object for prediction
vetiver_endpoint_sagemaker | Create a SageMaker model API endpoint object for prediction
vetiver_meta | Metadata constructors for 'vetiver_model()' object
vetiver_model | Create a vetiver object for deployment of a trained model
vetiver_pin_metrics | Update model metrics over time for monitoring
vetiver_pin_read | Read and write a trained model to a board of models
vetiver_pin_write | Read and write a trained model to a board of models
vetiver_plot_metrics | Plot model metrics over time for monitoring
vetiver_prepare_docker | Generate files necessary to build a Docker container for a vetiver model
vetiver_prepare_model | Model constructor methods
vetiver_prepare_model.default | Model constructor methods
vetiver_prepare_model.gam | Model constructor methods
vetiver_prepare_model.glm | Model constructor methods
vetiver_prepare_model.keras.engine.training.Model | Model constructor methods
vetiver_prepare_model.kproto | Model constructor methods
vetiver_prepare_model.Learner | Model constructor methods
vetiver_prepare_model.lm | Model constructor methods
vetiver_prepare_model.luz_module_fitted | Model constructor methods
vetiver_prepare_model.model_stack | Model constructor methods
vetiver_prepare_model.ranger | Model constructor methods
vetiver_prepare_model.recipe | Model constructor methods
vetiver_prepare_model.train | Model constructor methods
vetiver_prepare_model.workflow | Model constructor methods
vetiver_prepare_model.xgb.Booster | Model constructor methods
vetiver_pr_docs | Create a Plumber API to predict with a deployable 'vetiver_model()' object
vetiver_pr_post | Create a Plumber API to predict with a deployable 'vetiver_model()' object
vetiver_ptype | Create a vetiver input data prototype
vetiver_ptype.default | Create a vetiver input data prototype
vetiver_ptype.gam | Create a vetiver input data prototype
vetiver_ptype.glm | Create a vetiver input data prototype
vetiver_ptype.keras.engine.training.Model | Create a vetiver input data prototype
vetiver_ptype.kproto | Create a vetiver input data prototype
vetiver_ptype.Learner | Create a vetiver input data prototype
vetiver_ptype.lm | Create a vetiver input data prototype
vetiver_ptype.luz_module_fitted | Create a vetiver input data prototype
vetiver_ptype.model_stack | Create a vetiver input data prototype
vetiver_ptype.ranger | Create a vetiver input data prototype
vetiver_ptype.recipe | Create a vetiver input data prototype
vetiver_ptype.train | Create a vetiver input data prototype
vetiver_ptype.workflow | Create a vetiver input data prototype
vetiver_ptype.xgb.Booster | Create a vetiver input data prototype
vetiver_sm_build | Deploy a vetiver model API to Amazon SageMaker with modular functions
vetiver_sm_delete | Delete Amazon SageMaker model, endpoint, and endpoint configuration
vetiver_sm_endpoint | Deploy a vetiver model API to Amazon SageMaker with modular functions
vetiver_sm_model | Deploy a vetiver model API to Amazon SageMaker with modular functions
vetiver_type_convert | Convert new data at prediction time using input data prototype
vetiver_write_docker | Write a Dockerfile for a vetiver model
vetiver_write_plumber | Write a deployable Plumber file for a vetiver model