Fix model deployers docs formatting (#3356)

* Update model deployers documentation formatting and clarify lifecycle management methods

* Fix BentoML documentation formatting and remove trailing text

docs/book/component-guide/model-deployers/bentoml.md

@@ -49,7 +49,7 @@ The recommended flow to use the BentoML model deployer is to first [create a Ben
 
 ### Create a BentoML Service
 
-The first step to being able to deploy your models and use BentoML is to create a [bento service](https://docs.bentoml.com/en/latest/guides/services.html) which is the main logic that defines how your model will be served. The 
+The first step to being able to deploy your models and use BentoML is to create a [bento service](https://docs.bentoml.com/en/latest/guides/services.html) which is the main logic that defines how your model will be served.
 
 The following example shows how to create a basic bento service that will be used to serve a torch model. Learn more about how to specify the inputs and outputs for the APIs and how to use validators in the [Input and output types BentoML docs](https://docs.bentoml.com/en/latest/guides/iotypes.html)
 

docs/book/component-guide/model-deployers/model-deployers.md

@@ -86,13 +86,13 @@ zenml model-deployer register seldon --flavor=seldon \
    ```
 
 * Lifecycle Management: Provides mechanisms for comprehensive lifecycle management of model servers, including the ability to start, stop, and delete model servers, as well as to update existing servers with new model versions, thereby optimizing resource utilization and facilitating continuous delivery of model updates. Some core methods that can be used to interact with the remote model server include:
-
-`deploy_model` - Deploys a model to the serving environment and returns a Service object that represents the deployed model server.
-`find_model_server` - Finds and returns a list of Service objects that represent model servers that have been deployed to the serving environment, the 
-services are stored in the DB and can be used as a reference to know what and where the model is deployed.
-`stop_model_server` - Stops a model server that is currently running in the serving environment.
-`start_model_server` - Starts a model server that has been stopped in the serving environment.
-`delete_model_server` - Deletes a model server from the serving environment and from the DB.
+  - `deploy_model` - Deploys a model to the serving environment and returns a Service object that represents the deployed model server.
+  - `find_model_server` - Finds and returns a list of Service objects that
+    represent model servers that have been deployed to the serving environment,
+    the `services` are stored in the DB and can be used as a reference to know what and where the model is deployed.
+  - `stop_model_server` - Stops a model server that is currently running in the serving environment.
+  - `start_model_server` - Starts a model server that has been stopped in the serving environment.
+  - `delete_model_server` - Deletes a model server from the serving environment and from the DB.
 
 {% hint style="info" %}
 ZenML uses the Service object to represent a model server that has been deployed to a serving environment. The Service object is saved in the DB and can be used as a reference to know what and where the model is deployed. The Service object consists of 2 main attributes, the `config` and the `status`. The `config` attribute holds all the deployment configuration attributes required to create a new deployment, while the `status` attribute holds the operational status of the deployment, such as the last error message, the prediction URL, and the deployment status.