> For the complete documentation index, see [llms.txt](https://docs.bdb.ai/bdb-documentation/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.bdb.ai/bdb-documentation/data-science-lab/various-tabs-to-work-with/notebook/notebook-page/notebook-operations/transforms.md).
# Transforms
{% hint style="info" %}
*Check out a walk-through on how to use the Transform script inside Notebook.*
{% endhint %}
You can write or upload a script containing transform function to a Notebook and save a model based on it. You can also register the model as an API service. This entire process gets completed in the below given steps:
1. [**Saving and loading a Model with Transform script**](#saving-and-loading-a-model-with-transform-script)
2. [**Registering a Transform Model**](#registering-a-transform-model)
3. [**Publishing a Transform Model as API**](#publish-a-transform-model-as-api)
### Saving and loading a Model with Transform script
* Navigate to a Notebook.
* Add a Code cell (by default one cell gets added in a new Notebook).
* Write or provide a transform script to the cell.
* Run the cell.
* Add a new Code cell.
* Click the ***Save Model*** option to generate the code to save the desired model.
* Specify the model name and model type in the auto-generated script.
* Run the cell.
* The model gets saved under the Transform tab.
* Add a new Code cell.
* Use the checkbox given next to the saved model to load it.
* Run the cell.
* Add a new Code cell.
* Click the ***Transform*** option.
* Specify the train data.
* Run the cell.
* The data output appears below.
### Registering a Transform Model
* Open the ***Transforms*** tab inside a Notebook.
* Select the Register option for a listed transform.
* The Register Model dialog box opens to confirm the action.
* Click the ***Yes*** option.
* A confirmation message appears to inform completion of the action.
* The model gets registered and lists under the ***Registered*** list of the models.
* Open a pipeline workflow with a DS Lab model runner component.
* The registered model gets listed under the Meta Information tab of the DS Lab model runner component.
### Publishing a Transform Model as API
The Publish Model as API functionality gets completed in three steps:
1. [**Register a Transform Model as API**](#register-a-transform-model-as-api)
2. [**Register an API Client**](#register-an-api-client)
3. [**Pass Model Values in Postman**](#pass-model-values-in-postman)
#### Register a Transform Model as API
* Navigate to the ***Model*** tab.
* Select a saved model from the model list.
* Click the ***Register as API*** option.
* You will get directed to the ***Update Model*** page.
* Provide the ***Max Instance*** option.
* Click the ***Save and Register*** option.
* A confirmation message appears to indicate that the Model has been saved and registered (The icon for a registered model appears differently).
#### Register an API Client
* Navigate to the ***Admin Module*** from the ***Apps*** menu.
* Click on the ***API Client Registration*** option from the Admin panel.
#### 
* The ***API Client Registration*** form appears.
* Choose the ***Internal*** option from the ***Select*** drop-down.
* Provide the required details to register the API Client:
* Client Name
* Client Email
* App Name
* Request Per Hour
* Request Per Day
* Select App Type (Select ***Model As API*** option from the drop-down menu).
* Select the saved model using the ***Select the Services Entitled*** option.
* Click the ***Save*** option to save the client details.
* A confirmation message appears to inform about the completion of the client registration action.
* The API client gets registered. You can get the following details from the registered API Client Registration.
* App Name
* Client Id
* Client Secret Key
#### Pass Model values in Postman
* Navigate to the Postman application and select POST service.
* Open the ***Headers*** tab.
* Provide the clientid, clientsecret, and appname from the API Client Registration form as explained in the previous step.
* Open the ***Body*** tab.
* Provide the input dataframe.
* Click the ***Send*** option.
* The response will appear below.
* You can save the response by using the ***Save Response*** option.
