[DOCS] Adds a note about the intel and linux optimized versions of EL…

…SER and E5 are recommended. (#2750) (#2751)

(cherry picked from commit 6625cb1)

Co-authored-by: István Zoltán Szabó <istvan.szabo@elastic.co>

docs/en/stack/ml/nlp/ml-nlp-e5.asciidoc

@@ -46,6 +46,9 @@ You can download and deploy the E5 model either from
 **{ml-app}** > **Trained Models**, from **Search** > **Indices**, or by using 
 the Dev Console.
 
+NOTE: For most cases, the preferred version is the **Intel and Linux optimized**
+model, it is recommended to download and deploy that version.
+
 
 [discrete]
 [[trained-model-e5]]

docs/en/stack/ml/nlp/ml-nlp-elser.asciidoc

@@ -108,13 +108,17 @@ that walks through upgrading an index to ELSER V2.
 You can download and deploy ELSER either from **{ml-app}** > **Trained Models**, 
 from **Search** > **Indices**, or by using the Dev Console.
 
-IMPORTANT: You can deploy the model multiple times by assigning a unique
-deployment ID when starting the deployment. It enables you to have dedicated
-deployments for different purposes, such as search and ingest. By doing so, you
-ensure that the search speed remains unaffected by ingest workloads, and vice
-versa. Having separate deployments for search and ingest mitigates performance
-issues resulting from interactions between the two, which can be hard to
-diagnose.
+[NOTE]
+====
+* For most cases, the preferred version is the **Intel and Linux optimized**
+model, it is recommended to download and deploy that version.
+* You can deploy the model multiple times by assigning a unique deployment ID
+when starting the deployment. It enables you to have dedicated deployments for
+different purposes, such as search and ingest. By doing so, you ensure that the
+search speed remains unaffected by ingest workloads, and vice versa. Having
+separate deployments for search and ingest mitigates performance issues
+resulting from interactions between the two, which can be hard to diagnose.
+====
 
 
 [discrete]