add the documentations for the new ml_inference processor (#10652)

* add the documentations for the new ml_inference processor

Signed-off-by: Xun Zhang <xunzh@amazon.com>

* Doc review

Signed-off-by: Fanit Kolchina <kolchfa@amazon.com>

* Apply suggestions from code review

Signed-off-by: Nathan Bower <nbower@amazon.com>

---------

Signed-off-by: Xun Zhang <xunzh@amazon.com>
Signed-off-by: Fanit Kolchina <kolchfa@amazon.com>
Signed-off-by: Nathan Bower <nbower@amazon.com>
Co-authored-by: Fanit Kolchina <kolchfa@amazon.com>
Co-authored-by: Nathan Bower <nbower@amazon.com>

Author: 3 people

_data-prepper/pipelines/configuration/processors/add-entries.md

@@ -1,12 +1,12 @@
 ---
 layout: default
-title: add_entries
+title: Add entries
 parent: Processors
 grand_parent: Pipelines
-nav_order: 40
+nav_order: 10
 ---
 
-# add_entries
+# Add entries processor
 
 The `add_entries` processor adds entries to an event.
 

_data-prepper/pipelines/configuration/processors/aggregate.md

@@ -1,12 +1,12 @@
 ---
 layout: default
-title: aggregate
+title: Aggregate
 parent: Processors
 grand_parent: Pipelines
-nav_order: 41
+nav_order: 20
 ---
 
-# aggregate
+# Aggregate processor
 
 The `aggregate` processor groups events based on the values of `identification_keys`. Then, the processor performs an action on each group, helping reduce unnecessary log volume and creating aggregated logs over time. You can use existing actions or create your own custom aggregations using Java code.
 

_data-prepper/pipelines/configuration/processors/anomaly-detector.md

@@ -1,18 +1,18 @@
 ---
 layout: default
-title: anomaly_detector
+title: Anomaly detector
 parent: Processors
 grand_parent: Pipelines
-nav_order: 45
+nav_order: 30
 ---
 
-# anomaly_detector
+# Anomaly detector processor
 
-The anomaly detector processor takes structured data and runs anomaly detection algorithms on fields that you can configure in that data. The data must be either an integer or a real number for the anomaly detection algorithm to detect anomalies. Deploying the aggregate processor in a pipeline before the anomaly detector processor can help you achieve the best results, as the aggregate processor automatically aggregates events by key and keeps them on the same host. For example, if you are searching for an anomaly in latencies from a specific IP address and if all the events go to the same host, then the host has more data for these events. This additional data results in better training of the machine learning (ML) algorithm, which results in better anomaly detection. 
+The `anomaly_detector` processor takes structured data and runs anomaly detection algorithms on fields that you can configure in that data. The data must be either an integer or a real number for the anomaly detection algorithm to detect anomalies. Deploying the aggregate processor in a pipeline before the `anomaly_detector` processor can help you achieve the best results, as the aggregate processor automatically aggregates events by key and keeps them on the same host. For example, if you are searching for an anomaly in latencies from a specific IP address and if all the events go to the same host, then the host has more data for these events. This additional data results in better training of the machine learning (ML) algorithm, which results in better anomaly detection. 
 
 ## Configuration
 
-You can configure the anomaly detector processor by specifying a key and the options for the selected mode. You can use the following options to configure the anomaly detector processor.
+You can configure the `anomaly_detector` processor by specifying a key and the options for the selected mode. You can use the following options to configure the `anomaly_detector` processor.
 
 | Name | Required | Description |
 | :--- | :--- | :--- |
@@ -25,11 +25,11 @@ You can configure the anomaly detector processor by specifying a key and the opt
 
 ### Keys
 
-Keys that are used in the anomaly detector processor are present in the input event. For example, if the input event is `{"key1":value1, "key2":value2, "key3":value3}`, then any of the keys (such as `key1`, `key2`, `key3`) in that input event can be used as anomaly detector keys as long as their value (such as `value1`, `value2`, `value3`) is an integer or real number.
+Keys that are used in the `anomaly_detector` processor are present in the input event. For example, if the input event is `{"key1":value1, "key2":value2, "key3":value3}`, then any of the keys (such as `key1`, `key2`, `key3`) in that input event can be used as anomaly detector keys as long as their value (such as `value1`, `value2`, `value3`) is an integer or real number.
 
 ### random_cut_forest mode
 
-The random cut forest (RCF) ML algorithm is an unsupervised algorithm for detecting anomalous data points within a dataset. To detect anomalies, the anomaly detector processor uses the `random_cut_forest` mode.
+The random cut forest (RCF) ML algorithm is an unsupervised algorithm for detecting anomalous data points within a dataset. To detect anomalies, the `anomaly_detector` processor uses the `random_cut_forest` mode.
 
 | Name | Description |
 | :--- | :--- |
@@ -38,8 +38,8 @@ The random cut forest (RCF) ML algorithm is an unsupervised algorithm for detect
 RCF is an unsupervised ML algorithm for detecting anomalous data points within a dataset. OpenSearch Data Prepper uses RCF to detect anomalies in data by passing the values of the configured key to RCF. For example, when an event with a latency value of 11.5 is sent, the following anomaly event is generated:
 
 
- ```json
-  { "latency": 11.5, "deviation_from_expected":[10.469302736820003],"grade":1.0}
+```json
+{ "latency": 11.5, "deviation_from_expected":[10.469302736820003],"grade":1.0}
 ```
 
 In this example, `deviation_from_expected` is a list of deviations for each of the keys from their corresponding expected values, and `grade` is the anomaly grade that indicates the anomaly severity.
@@ -72,6 +72,6 @@ ad-pipeline:
             random_cut_forest:
 ```
 
-When you run the anomaly detector processor, the processor extracts the value for the `latency` key, and then passes the value through the RCF ML algorithm. You can configure any key that comprises integers or real numbers as values. In the following example, you can configure `bytes` or `latency` as the key for an anomaly detector. 
+When you run the `anomaly_detector` processor, the processor extracts the value for the `latency` key and then passes the value through the RCF ML algorithm. You can configure any key that comprises integers or real numbers as values. In the following example, you can configure `bytes` or `latency` as the key for an anomaly detector. 
 
 `{"ip":"1.2.3.4", "bytes":234234, "latency":0.2}`

_data-prepper/pipelines/configuration/processors/aws-lambda.md

@@ -1,14 +1,14 @@
 ---
 layout: default
-title: aws_lambda
+title: AWS Lambda
 parent: Processors
 grand_parent: Pipelines
-nav_order: 10
+nav_order: 40
 ---
 
-# aws_lambda integration for OpenSearch Data Prepper
+# AWS Lambda processor
 
-The [AWS Lambda](https://aws.amazon.com/lambda/) integration allows developers to use serverless computing capabilities within their OpenSearch Data Prepper pipelines for flexible event processing and data routing.
+The [AWS Lambda](https://aws.amazon.com/lambda/) integration allows you to use serverless computing capabilities within your OpenSearch Data Prepper pipelines for flexible event processing and data routing.
 
 ## AWS Lambda processor configuration
 
@@ -64,7 +64,7 @@ processors:
       lambda_when: "event['status'] == 'process'"
 
 ```
-{% include copy-curl.html %}
+{% include copy.html %}
 
 ## Usage
 
@@ -101,5 +101,4 @@ Integration tests for this plugin are executed separately from the main Data Pre
 ```
 ./gradlew :data-prepper-plugins:aws-lambda:integrationTest -Dtests.processor.lambda.region="us-east-1" -Dtests.processor.lambda.functionName="lambda_test_function"  -Dtests.processor.lambda.sts_role_arn="arn:aws:iam::123456789012:role/dataprepper-role
 ```
-
-{% include copy-curl.html %}
+{% include copy.html %}

_data-prepper/pipelines/configuration/processors/convert-entry-type.md

@@ -1,12 +1,12 @@
 ---
 layout: default
-title: convert_entry_type
+title: Convert entry type
 parent: Processors
 grand_parent: Pipelines
-nav_order: 47
+nav_order: 50
 ---
 
-# convert_entry_type
+# Convert entry type processor
 
 The `convert_entry_type` processor converts a value type associated with the specified key in a event to the specified type. It is a casting processor that changes the types of some fields in events. Some data must be converted to a different type, such as an integer to a double, or a string to an integer, so that it will pass the events through condition-based processors or perform conditional routing. 
 

_data-prepper/pipelines/configuration/processors/copy-values.md

@@ -1,12 +1,12 @@
 ---
 layout: default
-title: copy_values 
+title: Copy values 
 parent: Processors
 grand_parent: Pipelines
-nav_order: 48
+nav_order: 60
 ---
 
-# copy_values
+# Copy values processor
 
 The `copy_values` processor copies values within an event and is a [mutate event]({{site.url}}{{site.baseurl}}/data-prepper/pipelines/configuration/processors/mutate-event/) processor. 
 

_data-prepper/pipelines/configuration/processors/csv.md

@@ -1,12 +1,12 @@
 ---
 layout: default
-title: csv 
+title: CSV 
 parent: Processors
 grand_parent: Pipelines
-nav_order: 49
+nav_order: 70
 ---
 
-# csv
+# CSV processor
 
 The `csv` processor parses comma-separated values (CSVs) from the event into columns.
 

_data-prepper/pipelines/configuration/processors/date.md

@@ -1,12 +1,12 @@
 ---
 layout: default
-title: date
+title: Date
 parent: Processors
 grand_parent: Pipelines
-nav_order: 50
+nav_order: 80
 ---
 
-# date
+# Date processor
 
 
 The `date` processor adds a default timestamp to an event, parses timestamp fields, and converts timestamp information to the International Organization for Standardization (ISO) 8601 format. This timestamp information can be used as an event timestamp.

_data-prepper/pipelines/configuration/processors/decompress.md

@@ -1,12 +1,12 @@
 ---
 layout: default
-title: decompress
+title: Decompress
 parent: Processors
 grand_parent: Pipelines
-nav_order: 40
+nav_order: 90
 ---
 
-# decompress
+# Decompress processor
 
 The `decompress` processor decompresses any Base64-encoded compressed fields inside of an event.
 

_data-prepper/pipelines/configuration/processors/delay.md

@@ -1,12 +1,12 @@
 ---
 layout: default
-title: delay
+title: Delay
 parent: Processors
 grand_parent: Pipelines
-nav_order: 41
+nav_order: 100
 ---
 
-# delay
+# Delay processor
 
 This processor will add a delay into the processor chain. Typically, you should use this only for testing, experimenting, and debugging.