`install.packages("vetiver")`

# Getting Started

The vetiver framework for MLOps tasks is built for data science teams using R and/or Python, with a native, fluent experience for both. It is built to be extensible, with methods that can support many kinds of models.

## Installation

You can use vetiver with:

- a tidymodels workflow (including stacks)
- caret
- mlr3
- XGBoost
- ranger
`lm()`

and`glm()`

- GAMS fit with mgcv

You can install the released version of vetiver from CRAN:

And the development version from GitHub with:

```
# install.packages("devtools")
::install_github("tidymodels/vetiver-r") devtools
```

## Train a model

For this example, let’s work with data on fuel efficiency for cars to predict miles per gallon.

Let’s consider one kind of model supported by vetiver, a tidymodels workflow that encompasses both feature engineering and model estimation.

```
library(tidymodels)
<-
car_mod workflow(mpg ~ ., linear_reg()) %>%
fit(mtcars)
```

Let’s consider one kind of model supported by vetiver, a scikit-learn linear model.

```
from vetiver.data import mtcars
from sklearn.linear_model import LinearRegression
= LinearRegression().fit(mtcars.drop(columns="mpg"), mtcars["mpg"]) car_mod
```

This `car_mod`

object is a fitted model, with model parameters estimated using `mtcars`

.

## Create a vetiver model

We can create a `vetiver_model()`

in R or `VetiverModel()`

in Python from the trained model; a vetiver model object collects the information needed to store, version, and deploy a trained model.

```
library(vetiver)
<- vetiver_model(car_mod, "cars_mpg")
v v
```

```
── cars_mpg ─ <bundled_workflow> model for deployment
A lm regression modeling workflow using 10 features
```

```
from vetiver import VetiverModel
= VetiverModel(car_mod, model_name = "cars_mpg",
v = mtcars.drop(columns="mpg"))
ptype_data v.description
```

`"Scikit-learn <class 'sklearn.linear_model._base.LinearRegression'> model"`

Think of this vetiver model as a deployable model object.