You can store and version your model by choosing a pins “board” for it. Your board for model pins can be set up to use a local folder, Posit Connect, Amazon S3, and more. When we write the vetiver model to our board, the binary model object is stored on our board together with necessary metadata, like the packages needed to make a prediction and the model’s input data prototype for checking new data at prediction time.
Note
We’ll use a temporary board that will be automatically deleted for this demo, but for your real work, you will want to choose the best board for your particular infrastructure.
# A tibble: 2 × 3
version created hash
<chr> <dttm> <chr>
1 20231214T173705Z-8e55c 2023-12-14 17:37:05 8e55c
2 20231214T173707Z-458ca 2023-12-14 17:37:07 458ca
model_board.pin_versions("cars_mpg")
created hash version
0 2023-12-14 17:37:05 9dca2 20231214T173705Z-9dca2
1 2023-12-14 17:37:07 892ad 20231214T173707Z-892ad
The primary purpose of pins is to make it easy to share data artifacts, so depending on the board you choose, your pinned vetiver model can be shareable with your collaborators.