remove anchors

Author: charlotte-hoblik

deploy-manage/autoscaling.md

@@ -59,7 +59,7 @@ You can also have a look at our [autoscaling example](./autoscaling/ece-autoscal
 ::::
 
 ## Overview [ec-autoscaling-intro]
-$$$ece-autoscaling-intro$$$$$$ech-autoscaling-intro$$$When you first create a deployment it can be challenging to determine the amount of storage your data nodes will require. The same is relevant for the amount of memory and CPU that you want to allocate to your machine learning nodes. It can become even more challenging to predict these requirements for weeks or months into the future. In an ideal scenario, these resources should be sized to both ensure efficient performance and resiliency, and to avoid excess costs. Autoscaling can help with this balance by adjusting the resources available to a deployment automatically as loads change over time, reducing the need for monitoring and manual intervention.
+When you first create a deployment it can be challenging to determine the amount of storage your data nodes will require. The same is relevant for the amount of memory and CPU that you want to allocate to your machine learning nodes. It can become even more challenging to predict these requirements for weeks or months into the future. In an ideal scenario, these resources should be sized to both ensure efficient performance and resiliency, and to avoid excess costs. Autoscaling can help with this balance by adjusting the resources available to a deployment automatically as loads change over time, reducing the need for monitoring and manual intervention.
 
 ::::{note}
 Autoscaling is enabled for the Machine Learning tier by default for new deployments.
@@ -88,7 +88,7 @@ For any Elasticsearch component the number of availability zones is not affected
 
 ## When does autoscaling occur?[ec-autoscaling-factors]
 
-$$$ece-autoscaling-factors$$$$$$ech-autoscaling-factors$$$Several factors determine when data tiers or machine learning nodes are scaled.
+Several factors determine when data tiers or machine learning nodes are scaled.
 
 For a data tier, an autoscaling event can be triggered in the following cases:
 
@@ -103,14 +103,13 @@ On machine learning nodes, scaling is determined by an estimate of the memory an
 On a highly available deployment, autoscaling events are always applied to instances in each availability zone simultaneously, to ensure consistency.
 
 ## Notifications[ec-autoscaling-notifications]
-$$$ece-autoscaling-notifications$$$$$$ech-autoscaling-notifications$$$
 In the event that a data tier or machine learning node scales up to its maximum possible size, you’ll receive an email, and a notice also appears on the deployment overview page prompting you to adjust your autoscaling settings to ensure optimal performance.
 
 In {{ece}} deployments, a warning is also issued in the ECE `service-constructor` logs with the field `labels.autoscaling_notification_type` and a value of `data-tier-at-limit` (for a fully scaled data tier) or `ml-tier-at-limit` (for a fully scaled machine learning node). The warning is indexed in the `logging-and-metrics` deployment, so you can use that event to [configure an email notification](../explore-analyze/alerts-cases/watcher.md).
 
 ## Restrictions and limitations[ec-autoscaling-restrictions]
 
-$$$ece-autoscaling-restrictions$$$$$$ech-autoscaling-restrictions$$$The following are known limitations and restrictions with autoscaling:
+The following are known limitations and restrictions with autoscaling:
 
 * Autoscaling will not run if the cluster is unhealthy or if the last Elasticsearch plan failed.
 
@@ -125,7 +124,7 @@ In {{ece}}, the following additional limitations apply:
 
 ## Enable or disable autoscaling[ec-autoscaling-enable]
 
-$$$ece-autoscaling-enable$$$$$$ech-autoscaling-enable$$$To enable or disable autoscaling on a deployment:
+To enable or disable autoscaling on a deployment:
 
 1. Log in to the ECE [Cloud UI]((/deploy-manage/deploy/cloud-enterprise/log-into-cloud-ui.md) or  [{{ecloud}} Console](https://cloud.elastic.co?page=docs&placement=docs-body).
 
@@ -144,7 +143,7 @@ When autoscaling has been disabled, you need to adjust the size of data tiers an
 
 ## Update your autoscaling settings[ec-autoscaling-update]
 
-$$$ece-autoscaling-update$$$$$$ech-autoscaling-update$$$Each autoscaling setting is configured with a default value. You can adjust these if necessary, as follows:
+Each autoscaling setting is configured with a default value. You can adjust these if necessary, as follows:
 
 1. **Log in** to the console.
 

deploy-manage/autoscaling/trained-model-autoscaling.md

@@ -25,7 +25,6 @@ Trained model autoscaling is available for both serverless and Cloud deployments
 Security and Observability projects are only charged for data ingestion and retention. They are not charged for processing power (VCU usage), which is used for more complex operations, like running advanced search models. For example, in Search projects, models such as ELSER require significant processing power to provide more accurate search results.
 
 ## Enabling autoscaling through APIs - adaptive allocations [enabling-autoscaling-through-apis-adaptive-allocations]
-$$$nlp-model-adaptive-allocations$$$
 
 Model allocations are independent units of work for NLP tasks. If you set the numbers of threads and allocations for a model manually, they remain constant even when not all the available resources are fully used or when the load on the model requires more resources. Instead of setting the number of allocations manually, you can enable adaptive allocations to set the number of allocations based on the load on the process. This can help you to manage performance and cost more easily. (Refer to the [pricing calculator](https://cloud.elastic.co/pricing) to learn more about the possible costs.)
 
@@ -54,7 +53,6 @@ You can optimize your model deployment for typical use cases, such as search and
 * If you want to optimize for search, set the number of threads to greater than `1`. Increasing the number of threads will make the search processes more performant.
 
 ## Enabling autoscaling in {{kib}} - adaptive resources [enabling-autoscaling-in-kibana-adaptive-resources]
-$$$nlp-model-adaptive-resources$$$
 
 You can enable adaptive resources for your models when starting or updating the model deployment. Adaptive resources make it possible for {{es}} to scale up or down the available resources based on the load on the process. This can help you to manage performance and cost more easily. When adaptive resources are enabled, the number of vCPUs that the model deployment uses is set automatically based on the current load. When the load is high, the number of vCPUs that the process can use is automatically increased. When the load is low, the number of vCPUs that the process can use is automatically decreased.