[DOCS] Augments job validation tips (#21184)

docs/ml/creating-jobs.asciidoc

@@ -1,6 +1,6 @@
 [role="xpack"]
 [[ml-jobs]]
-== Creating Machine Learning Jobs
+== Creating machine learning jobs
 
 Machine learning jobs contain the configuration information and metadata
 necessary to perform an analytics task.

docs/ml/job-tips.asciidoc

@@ -1,8 +1,8 @@
 [role="xpack"]
 [[job-tips]]
-=== Machine Learning Job Tips
+=== Machine learning job tips
 ++++
-<titleabbrev>Job Tips</titleabbrev>
+<titleabbrev>Job tips</titleabbrev>
 ++++
 
 When you are creating a job in {kib}, the job creation wizards can provide
@@ -10,15 +10,19 @@ advice based on the characteristics of your data. By heeding these suggestions,
 you can create jobs that are more likely to produce insightful {ml} results.
 
 [[bucket-span]]
-==== Bucket Span
+==== Bucket span
 
 The bucket span is the time interval that {ml} analytics use to summarize and
 model data for your job. When you create a job in {kib}, you can choose to
-estimate a bucket span value based on your data characteristics. Typically, the
-estimated value is between 5 minutes to 1 hour. If you choose a value that is
-larger than one day or is significantly different than the estimated value, you
-receive an informational message. For more information about choosing an
-appropriate bucket span, see {xpack-ref}/ml-buckets.html[Buckets].
+estimate a bucket span value based on your data characteristics. 
+
+NOTE: The bucket span must contain a valid time interval. For more information, 
+see {ref}/ml-job-resource.html#ml-analysisconfig[Analysis configuration objects].
+
+If you choose a value that is larger than one day or is significantly different 
+than the estimated value, you receive an informational message. For more 
+information about choosing an appropriate bucket span, see 
+{xpack-ref}/ml-buckets.html[Buckets].
 
 [[cardinality]]
 ==== Cardinality
@@ -30,16 +34,26 @@ detect when users are accessing resources differently than they usually do.
 
 If the field that you use to split your data has many different values, the
 job uses more memory resources. In particular, if the cardinality of the
-`partition_field_name` is greater than 100, you are advised to consider
-alternative options such as population analysis.
+`by_field_name`, `over_field_name`, or `partition_field_name` is greater than 
+1000, you are advised that there might be high memory usage. 
 
 Likewise if you are performing population analysis and the cardinality of the
 `over_field_name` is below 10, you are advised that this might not be a suitable
-field to use.
-
-For more information, see
+field to use. For more information, see
 {xpack-ref}/ml-configuring-pop.html[Performing Population Analysis].
 
+[[detectors]]
+==== Detectors
+
+Each job must have one or more _detectors_. A detector applies an analytical 
+function to specific fields in your data. If your job does not contain a 
+detector or the detector does not contain a 
+{stack-ov}/ml-functions.html[valid function], you receive an error.
+
+If a job contains duplicate detectors, you also receive an error. Detectors are 
+duplicates if they have the same `function`, `field_name`, `by_field_name`, 
+`over_field_name` and `partition_field_name`. 
+
 [[influencers]]
 ==== Influencers
 
@@ -60,3 +74,50 @@ do not need more than three. If you pick many influencers, the results can be
 overwhelming and there is a small overhead to the analysis.
 
 The job creation wizards in {kib} can suggest which fields to use as influencers.
+
+[[model-memory-limits]]
+==== Model memory limits
+
+For each job, you can optionally specify a `model_memory_limit`, which is the 
+approximate maximum amount of memory resources that are required for analytical 
+processing. The default value is 1 GB. Once this limit is approached, data 
+pruning becomes more aggressive. Upon exceeding this limit, new entities are not 
+modeled. 
+
+You can also optionally specify the `xpack.ml.max_model_memory_limit` setting. 
+By default, it's not set, which means there is no upper bound on the acceptable 
+`model_memory_limit` values in your jobs. 
+
+TIP: If you set the `model_memory_limit` too high, it will be impossible to open 
+the job; jobs cannot be allocated to nodes that have insufficient memory to run 
+them.
+
+If the estimated model memory limit for a job is greater than the model memory 
+limit for the job or the maximum model memory limit for the cluster, the job 
+creation wizards in {kib} generate a warning. If the estimated memory 
+requirement is only a little higher than the `model_memory_limit`, the job will 
+probably produce useful results. Otherwise, the actions you take to address 
+these warnings vary depending on the resources available in your cluster:
+
+* If you are using the default value for the `model_memory_limit` and the {ml} 
+nodes in the cluster have lots of memory, the best course of action might be to 
+simply increase the job's `model_memory_limit`. Before doing this, however, 
+double-check that the chosen analysis makes sense. The default 
+`model_memory_limit` is relatively low to avoid accidentally creating a job that 
+uses a huge amount of memory.
+* If the {ml} nodes in the cluster do not have sufficient memory to accommodate 
+a job of the estimated size, the only options are:
+** Add bigger {ml} nodes to the cluster, or 
+** Accept that the job will hit its memory limit and will not necessarily find 
+all the anomalies it could otherwise find.
+
+If you are using {ece} or the hosted Elasticsearch Service on Elastic Cloud, 
+`xpack.ml.max_model_memory_limit` is set to prevent you from creating jobs 
+that cannot be allocated to any {ml} nodes in the cluster. If you find that you 
+cannot increase `model_memory_limit` for your {ml} jobs, the solution is to 
+increase the size of the {ml} nodes in your cluster.
+
+For more information about the `model_memory_limit` property and the 
+`xpack.ml.max_model_memory_limit` setting, see 
+{ref}/ml-job-resource.html#ml-analysisconfig[Analysis limits] and 
+{ref}/ml-settings.html[Machine learning settings].