[DOCS] Sync saved objects when deploying model

docs/en/stack/ml/nlp/ml-nlp-deploy-models.asciidoc

@@ -81,6 +81,11 @@ Alternatively, you can use the
 {ref}/start-trained-model-deployment.html[start trained model deployment API] or
 specify the `--start` option when you run the `eland_import_hub_model` script.
 
+NOTE: Since eland uses APIs to deploy the models, you cannot see the models in
+{kib} until the saved objects are synchronized. You can follow the prompts in
+{kib}, wait for automatic synchronization, or use the
+{kibana-ref}/machine-learning-api-sync.html[sync {ml} saved objects API].
+
 When you deploy the model, it is allocated to all available {ml} nodes. The
 model is loaded into memory in a native process that encapsulates `libtorch`,
 which is the underlying machine learning library of PyTorch.

docs/en/stack/ml/setup.asciidoc

@@ -92,10 +92,10 @@ same space as your {ml} jobs. These can be configured in **{stack-manage-app}**
 > _{kib}_ > _{data-sources-caps}_.
 
 
-Each {ml} job can be assigned to all, one or multiple spaces. This can be 
-configured in **{stack-manage-app}** > _Alerts and Insights_ > 
-_Machine Learning Jobs_. To assign a job to a space, select the spaces icon 
-shown in the job list.
+Each {ml} job and trained model can be assigned to all, one, or multiple spaces.
+This can be configured in **{stack-manage-app} > Alerts and Insights > Machine Learning**.
+You can edit the spaces that a job or model is assigned to by clicking the
+icons in the **Spaces** column.
 
 [role="screenshot"]
 image::assign-job-spaces.jpg["Assign machine learning jobs to spaces"]