Removed a trailing "`" in the xpack.ml.model_repository parameter

docs/en/stack/ml/anomaly-detection/anomaly-how-tos.asciidoc

@@ -13,6 +13,7 @@ The guides in this section describe some best practices for generating useful
 * <<ml-configuring-aggregation, Aggregating data for faster performance>>
 * <<ml-configuring-transform, Using runtime fields in {dfeeds}>>
 * <<ml-configuring-detector-custom-rules>>
+* <<ml-configuring-categories>>
 * <<ml-reverting-model-snapshot>>
 * <<geographic-anomalies>>
 * <<mapping-anomalies>>

docs/en/stack/ml/anomaly-detection/geographic-anomalies.asciidoc

@@ -211,6 +211,11 @@ behavior:
 image::images/ecommerce-anomaly-explorer-geopoint.jpg[A screenshot of an anomalous event in the eCommerce data in Anomaly Explorer]
 // NOTE: This is an autogenerated screenshot. Do not edit it directly.
 
+A "typical" value indicates a centroid of a cluster of previously observed 
+locations that is closest to the "actual" location at that time. For example, 
+there may be one centroid near the user's home and another near the user's 
+work place since there are many records associated with these distinct locations. 
+
 Likewise, there are time periods in the web logs sample data where there are
 both unusually high sums of data transferred and unusual geographical
 coordinates:

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

@@ -36,6 +36,8 @@ include::{es-repo-dir}/ml/anomaly-detection/ml-configuring-transform.asciidoc[le
 
 include::{es-repo-dir}/ml/anomaly-detection/ml-configuring-detector-custom-rules.asciidoc[leveloffset=+2]
 
+include::ml-detect-categories.asciidoc[leveloffset=+2]
+
 include::ml-revert-model-snapshot.asciidoc[leveloffset=+2]
 
 include::geographic-anomalies.asciidoc[leveloffset=+2]

docs/en/stack/ml/anomaly-detection/ml-detect-categories.asciidoc

@@ -0,0 +1,253 @@
+[[ml-configuring-categories]]
+= Detecting anomalous categories of data
+
+Categorization is a {ml} process that tokenizes a text field, clusters similar data together, and classifies it into categories.
+It works best on machine-written messages and application output that typically consist of repeated elements.
+<<categorization-jobs, Categorization jobs>> enable you to find anomalous behavior in your categorized data.
+Categorization is not natural language processing (NLP).
+When you create a categorization {anomaly-job}, the {ml} model learns what volume and pattern is normal for each category over time.
+You can then detect anomalies and surface rare events or unusual types of messages by using <<ml-count-functions,count>> or <<ml-rare-functions,rare>> functions.
+Categorization works well on finite set of possible messages, for example: 
+
+[source,js]
+----------------------------------
+{"@timestamp":1549596476000,
+"message":"org.jdbi.v2.exceptions.UnableToExecuteStatementException: com.mysql.jdbc.exceptions.MySQLTimeoutException: Statement cancelled due to timeout or client request [statement:\"SELECT id, customer_id, name, force_disabled, enabled FROM customers\"]",
+"type":"logs"}
+----------------------------------
+//NOTCONSOLE
+
+
+[discrete]
+[[categ-recommendations]]
+== Recommendations
+
+* Categorization is tuned to work best on data like log messages by taking token order into account, including stop words, and not considering synonyms in its analysis.
+Use machine-written messages for categorization analysis.
+* Complete sentences in human communication or literary text (for example email, wiki pages, prose, or other human-generated content) can be extremely diverse in structure.
+Since categorization is tuned for machine data, it gives poor results for human-generated data.
+It would create so many categories that they couldn’t be handled effectively.
+Avoid using human-generated data for categorization analysis.
+
+[discrete]
+[[creating-categorization-jobs]]
+== Creating categorization jobs
+
+. In {kib}, navigate to **{ml-app} > Anomaly Detection > Jobs**.
+. Click **Create {anomaly-jobs}**, select the {data-view} you want to analyze.
+. Select the **Categorization** wizard from the list.
+. Choose a categorization detector - it's the `count` function in this example - and the field you want to categorize - the `message` field in this example.
++
+--
+[role="screenshot"]
+image::images/categorization-wizard.png[Creating a categorization job in Kibana]
+--
+. Click **Next**.
+. Provide a job ID and click **Next**.
+. If the validation is successful, click **Next** to review the summary of the job creation.
+. Click **Create job**.
+
+This example job generates categories from the contents of the `message` field and uses the `count` function to determine when certain categories are occurring at anomalous rates.
+
+[%collapsible]
+.API example
+====
+[source,console]
+----------------------------------
+PUT _ml/anomaly_detectors/it_ops_app_logs
+{
+  "description" : "IT ops application logs",
+  "analysis_config" : {
+    "categorization_field_name": "message",<1>
+    "bucket_span":"30m",
+    "detectors" :[{
+      "function":"count",
+      "by_field_name": "mlcategory"<2>
+    }]
+  },
+  "data_description" : {
+    "time_field":"@timestamp"
+  }
+}
+----------------------------------
+// TEST[skip:needs-licence]
+<1> This field is used to derive categories.
+<2> The categories are used in a detector by setting `by_field_name`, `over_field_name`, or `partition_field_name` to the keyword `mlcategory`.
+If you do not specify this keyword in one of those properties, the API request fails.
+====
+
+
+[discrete]
+[[categorization-job-results]]
+=== Viewing the job results
+
+Use the **Anomaly Explorer** in {kib} to view the analysis results:
+
+[role="screenshot"]
+image::images/ml-category-anomalies.png["Categorization results in the Anomaly Explorer"]
+
+For this type of job, the results contain extra information for each anomaly: the name of the category (for example, `mlcategory 2`) and examples of the messages in that category.
+You can use these details to investigate occurrences of unusually high message counts.
+
+
+[discrete]
+[[advanced-categorization-options]]
+=== Advanced configuration options
+
+If you use the advanced {anomaly-job} wizard in {kib} or the {ref}/ml-put-job.html[create {anomaly-jobs} API], there are additional configuration options.
+For example, the optional `categorization_examples_limit` property specifies the maximum number of examples that are stored in memory and in the results data store for each category.
+The default value is `4`.
+Note that this setting does not affect the categorization; it just affects the list of visible examples.
+If you increase this value, more examples are available, but you must have more storage available.
+If you set this value to `0`, no examples are stored.
+
+Another advanced option is the `categorization_filters` property, which can contain an array of regular expressions.
+If a categorization field value matches the regular expression, the portion of the field that is matched is not taken into consideration when defining categories.
+The categorization filters are applied in the order they are listed in the job configuration, which enables you to disregard multiple sections of the categorization field value.
+In this example, you might create a filter like `[ "\\[statement:.*\\]"]` to remove the SQL statement from the categorization algorithm.
+
+
+[discrete]
+[[ml-per-partition-categorization]]
+== Per-partition categorization
+
+If you enable per-partition categorization, categories are determined independently for each partition.
+For example, if your data includes messages from multiple types of logs from different applications, you can use a field like the ECS {ecs-ref}/ecs-event.html[`event.dataset` field] as the `partition_field_name` and categorize the messages for each type of log separately.
+
+If your job has multiple detectors, every detector that uses the `mlcategory` keyword must also define a `partition_field_name`.
+You must use the same `partition_field_name` value in all of these detectors.
+Otherwise, when you create or update a job and enable per-partition categorization, it fails.
+
+When per-partition categorization is enabled, you can also take advantage of a `stop_on_warn` configuration option.
+If the categorization status for a partition changes to `warn`, it doesn't categorize well and can cause unnecessary resource usage.
+When you set `stop_on_warn` to `true`, the job stops analyzing these problematic partitions.
+You can thus avoid an ongoing performance cost for partitions that are unsuitable for categorization.
+
+
+[discrete]
+[[ml-configuring-analyzer]]
+== Customizing the categorization analyzer
+
+Categorization uses English dictionary words to identify log message categories.
+By default, it also uses English tokenization rules.
+For this reason, if you use the default categorization analyzer, only English language log messages are supported, as described in the <<ml-limitations>>.
+
+If you use the categorization wizard in {kib}, you can see which categorization analyzer it uses and highlighted examples of the tokens that it identifies.
+You can also change the tokenization rules by customizing the way the categorization field values are interpreted:
+
+[role="screenshot"]
+image::images/ml-category-analyzer.png["Editing the categorization analyzer in Kibana"]
+
+The categorization analyzer can refer to a built-in {es} analyzer or a combination of zero or more character filters, a tokenizer, and zero or more token filters.
+In this example, adding a {ref}/analysis-pattern-replace-charfilter.html[`pattern_replace` character filter] achieves the same behavior as the `categorization_filters` job configuration option described earlier.
+For more details about these properties, refer to the {ref}/ml-put-job.html#ml-put-job-request-body[`categorization_analyzer` API object].
+
+If you use the default categorization analyzer in {kib} or omit the `categorization_analyzer` property from the API, the following default values are used:
+
+[source,console]
+--------------------------------------------------
+POST _ml/anomaly_detectors/_validate
+{
+  "analysis_config" : {
+    "categorization_analyzer" : {
+      "char_filter" : [
+        "first_line_with_letters"
+      ],
+      "tokenizer" : "ml_standard",
+      "filter" : [
+        { "type" : "stop", "stopwords": [
+          "Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday", "Sunday",
+          "Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun",
+          "January", "February", "March", "April", "May", "June", "July", "August", "September", "October", "November", "December",
+          "Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec",
+          "GMT", "UTC"
+        ] }
+      ]
+    },
+    "categorization_field_name": "message",
+    "detectors" :[{
+      "function":"count",
+      "by_field_name": "mlcategory"
+    }]
+  },
+  "data_description" : {
+  }
+}
+--------------------------------------------------
+
+If you specify any part of the `categorization_analyzer`, however, any omitted sub-properties are _not_ set to default values.
+
+The `ml_standard` tokenizer and the day and month stopword filter are almost equivalent to the following analyzer, which is defined using only built-in {es} {ref}/analysis-tokenizers.html[tokenizers] and {ref}/analysis-tokenfilters.html[token filters]:
+
+[source,console]
+----------------------------------
+PUT _ml/anomaly_detectors/it_ops_new_logs
+{
+  "description" : "IT Ops Application Logs",
+  "analysis_config" : {
+    "categorization_field_name": "message",
+    "bucket_span":"30m",
+    "detectors" :[{
+      "function":"count",
+      "by_field_name": "mlcategory",
+      "detector_description": "Unusual message counts"
+    }],
+    "categorization_analyzer":{
+      "char_filter" : [
+        "first_line_with_letters" <1>
+      ],
+      "tokenizer": {
+        "type" : "simple_pattern_split",
+        "pattern" : "[^-0-9A-Za-z_./]+" <2>
+      },
+      "filter": [
+        { "type" : "pattern_replace", "pattern": "^[0-9].*" }, <3>
+        { "type" : "pattern_replace", "pattern": "^[-0-9A-Fa-f.]+$" }, <4>
+        { "type" : "pattern_replace", "pattern": "^[^0-9A-Za-z]+" }, <5>
+        { "type" : "pattern_replace", "pattern": "[^0-9A-Za-z]+$" }, <6>
+        { "type" : "stop", "stopwords": [
+          "",
+          "Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday", "Sunday",
+          "Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun",
+          "January", "February", "March", "April", "May", "June", "July", "August", "September", "October", "November", "December",
+          "Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec",
+          "GMT", "UTC"
+        ] }
+      ]
+    }
+  },
+  "analysis_limits":{
+    "categorization_examples_limit": 5
+  },
+  "data_description" : {
+    "time_field":"time",
+    "time_format": "epoch_ms"
+  }
+}
+----------------------------------
+// TEST[skip:needs-licence]
+
+<1> Only consider the first line of the message with letters for categorization purposes.
+<2> Tokens consist of hyphens, digits, letters, underscores, dots and slashes.
+<3> By default, categorization ignores tokens that begin with a digit.
+<4> By default, categorization ignores tokens that are hexadecimal numbers.
+<5> Underscores, hyphens, and dots are removed from the beginning of tokens.
+<6> Underscores, hyphens, and dots are also removed from the end of tokens.
+
+The key difference between the default `categorization_analyzer` and this example analyzer is that using the `ml_standard` tokenizer is several times faster.
+The `ml_standard` tokenizer also tries to preserve URLs, Windows paths and email addresses as single tokens.
+Another difference in behavior is that the custom analyzer does not include accented letters in tokens whereas the `ml_standard` tokenizer does.
+This could be fixed by using more complex regular expressions.
+
+If you are categorizing non-English messages in a language where words are separated by spaces, you might get better results if you change the day or month words in the stop token filter to the appropriate words in your language.
+If you are categorizing messages in a language where words are not separated by spaces, you must use a different tokenizer as well in order to get sensible categorization results.
+
+It is important to be aware that analyzing for categorization of machine generated log messages is a little different from tokenizing for search.
+Features that work well for search, such as stemming, synonym substitution, and lowercasing are likely to make the results of categorization worse.
+However, to drill down from {ml} results to work correctly, the tokens the categorization analyzer produces must be similar to those produced by the search analyzer.
+If they are sufficiently similar, when you search for the tokens that the categorization analyzer produces then you find the original document that the categorization field value came from.
+
+
+
+
+

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

@@ -1,10 +1,8 @@
 ["appendix",role="exclude",id="ootb-ml-jobs-metrics-ui"]
 = Metrics {anomaly-detect} configurations
 
-These {anomaly-jobs} can be created in the
-{observability-guide}/analyze-metrics.html[{metrics-app}] in {kib}. For more
-information about their usage, refer to
-{observability-guide}/inspect-metric-anomalies.html[Inspect metric anomalies].
+These {anomaly-jobs} can be created in the {observability-guide}/analyze-metrics.html[{infrastructure-app}] in {kib}.
+For more information about their usage, refer to {observability-guide}/inspect-metric-anomalies.html[Inspect metric anomalies].
 
 // tag::metrics-jobs[]
 [discrete]

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

@@ -94,7 +94,8 @@ eland_import_hub_model \
 <<ml-nlp-authentication>> to learn more.
 <3> Specify the identifier for the model in the Hugging Face model hub.
 <4> Specify the type of NLP task. Supported values are `fill_mask`, `ner`,
-`text_classification`, `text_embedding`, and `zero_shot_classification`.
+`question_answering`, `text_classification`, `text_embedding`, `text_expansion`,
+`text_similarity`, and `zero_shot_classification`.
 
 For more details, refer to 
 https://www.elastic.co/guide/en/elasticsearch/client/eland/current/machine-learning.html#ml-nlp-pytorch.
@@ -112,7 +113,7 @@ $ docker run -it --rm --network host docker.elastic.co/eland/eland
 The `eland_import_hub_model` script can be run directly in the docker command:
 
 ```bash
-docker run -it --rm elastic/eland \
+docker run -it --rm docker.elastic.co/eland/eland \
     eland_import_hub_model \
       --url $ELASTICSEARCH_URL \
       --hub-model-id elastic/distilbert-base-uncased-finetuned-conll03-english \