[DOCS] Add model memory tip for ML modules (elastic#1271)

Author: lcawl

docs/en/stack/ml/anomaly-detection/anomaly-detection-scale.asciidoc

@@ -159,18 +159,33 @@ start the {dfeed} for the change to be applied.
 == 7. Set the model memory limit
 
 The `model_memory_limit` job configuration option sets the approximate maximum 
-amount of memory resources required for analytical processing. If this variable 
-is set too low for the job and the limit is approached, data pruning becomes 
-more aggressive. Upon exceeding this limit, new entities are not modeled.
-
-Use model memory estimation to have a better picture of the memory needs of the 
-model. Model memory estimation happens automatically when you create the job in 
-{kib} or you can call the 
-{ref}/ml-estimate-model-memory.html[Estimate {anomaly-jobs} model memory API] 
-manually. The estimation is based on the analysis configuration details for the 
-job and cardinality estimates for the fields it references. You can update the 
-memory settings of an existing job, but the job must be closed.
-
+amount of memory resources required for analytical processing. When you create
+an {anomaly-job} in {kib}, it provides an estimate for this limit. The estimate 
+is based on the analysis configuration details for the job and cardinality 
+estimates, which are derived by running aggregations on the source indices as
+they exist at that specific point in time. 
+
+If you change the resources available on your {ml} nodes or make significant 
+changes to the characteristics or cardinality of your data, the model memory 
+requirements might also change. You can update the model memory limit for a job 
+while it is closed. If you want to decrease the limit below the current model 
+memory usage, however, you must clone and re-run the job.
+
+TIP: You can view the current model size statistics with the
+{ref}/ml-get-job-stats.html[get {anomaly-job} stats] and
+{ref}/ml-get-snapshot.html[get model snapshots] APIs. You can also obtain a
+model memory limit estimate at any time by running the
+{ref}/ml-estimate-model-memory.html[estimate {anomaly-jobs} model memory API].  
+However, you must provide your own cardinality estimates.
+
+As a job approaches its model memory limit, the memory status is `soft_limit`
+and older models are more aggressively pruned to free up space. If you have
+categorization jobs, no further examples are stored. When a job exceeds its 
+limit, the memory status is `hard_limit` and the job no longer models new 
+entities. It is therefore important to have appropriate memory model limits for 
+each job. If you reach the hard limit and are concerned about the missing data,
+ensure that you have adequate resources then clone and re-run the job with a 
+larger model memory limit.
 
 [discrete]
 [[pre-aggregate-data]]

docs/en/stack/ml/anomaly-detection/ootb-ml-jobs.asciidoc

@@ -26,4 +26,15 @@ be created via the related solution UI in {kib}.
 
 NOTE: The configurations are only available if data exists that matches the 
 queries specified in the manifest files. These recognizer queries are linked in 
-the descriptions of the individual configurations.
+the descriptions of the individual configurations.
+
+[discrete]
+[[ootb-ml-model-memory]]
+== Model memory considerations
+
+By default, these jobs have `model_memory_limit` values that are deemed 
+appropriate for typical user environments and data characteristics. If your
+environment or your data is atypical and your jobs reach a memory status value
+of `soft_limit` or `hard_limit`, you might need to update the model memory
+limits. For more information, see
+<<set-model-memory-limit,Working with {anomaly-detect} at scale>>.