Docs update

docs/source/modules/examples/digital_fingerprinting/dfp_deployment.md

@@ -21,54 +21,54 @@ This module function sets up a pipeline builder instance.
 
 ### Configurable Parameters
 
-- training_options (dict): Options for the training pipeline module, including:
-    - timestamp_column_name (str): Name of the timestamp column used in the data
-    - cache_dir (str): Directory to cache the rolling window data
-    - batching_options (dict): Options for batching the data, including:
-        - end_time (datetime|str): End time of the time window
-        - iso_date_regex_pattern (str): Regex pattern for ISO date matching
-        - parser_kwargs (dict): Additional arguments for the parser
-        - period (str): Time period for grouping files
-        - sampling_rate_s (int): Sampling rate in seconds
-        - start_time (datetime|str): Start time of the time window
-    - user_splitting_options (dict): Options for splitting the data by user, including:
-        - fallback_username (str): User ID to use if user ID not found (default: 'generic_user')
-        - include_generic (bool): Include generic user ID in output (default: False)
-        - include_individual (bool): Include individual user IDs in output (default: False)
-        - only_users (list): List of user IDs to include in output, others will be excluded (default: [])
-        - skip_users (list): List of user IDs to exclude from output (default: [])
-        - timestamp_column_name (str): Name of column containing timestamps (default: 'timestamp')
-        - userid_column_name (str): Name of column containing user IDs (default: 'username')
-    - stream_aggregation_options (dict): Options for aggregating the data by stream
-    - preprocessing_options (dict): Options for preprocessing the data
-    - dfencoder_options (dict): Options for configuring the data frame encoder, used for training the model
-    - mlflow_writer_options (dict): Options for the MLflow model writer, responsible for saving the trained model,
+- `training_options` (dict): Options for the training pipeline module, including:
+    - `timestamp_column_name` (str): Name of the timestamp column used in the data
+    - `cache_dir` (str): Directory to cache the rolling window data
+    - `batching_options` (dict): Options for batching the data, including:
+        - `end_time` (datetime|str): End time of the time window
+        - `iso_date_regex_pattern` (str): Regex pattern for ISO date matching
+        - `parser_kwargs` (dict): Additional arguments for the parser
+        - `period` (str): Time period for grouping files
+        - `sampling_rate_s` (int): Sampling rate in seconds
+        - `start_time` (datetime|str): Start time of the time window
+    - `user_splitting_options` (dict): Options for splitting the data by user, including:
+        - `fallback_username` (str): User ID to use if user ID not found (default: 'generic_user')
+        - `include_generic` (bool): Include generic user ID in output (default: False)
+        - `include_individual` (bool): Include individual user IDs in output (default: False)
+        - `only_users` (list): List of user IDs to include in output, others will be excluded (default: [])
+        - `skip_users` (list): List of user IDs to exclude from output (default: [])
+        - `timestamp_column_name` (str): Name of column containing timestamps (default: 'timestamp')
+        - `userid_column_name` (str): Name of column containing user IDs (default: 'username')
+    - `stream_aggregation_options` (dict): Options for aggregating the data by stream
+    - `preprocessing_options` (dict): Options for preprocessing the data
+    - `dfencoder_options` (dict): Options for configuring the data frame encoder, used for training the model
+    - `mlflow_writer_options` (dict): Options for the MLflow model writer, responsible for saving the trained model,
       including:
-        - model_name_formatter (str): Format string for the model name, e.g. "model_{timestamp}"
-        - experiment_name_formatter (str): Format string for the experiment name, e.g. "experiment_{timestamp}"
-        - timestamp_column_name (str): Name of the timestamp column used in the data
-        - conda_env (dict): Conda environment settings, including:
-            - channels (list): List of channels to use for the environment
-            - dependencies (list): List of dependencies for the environment
-            - pip (list): List of pip packages to install in the environment
-            - name (str): Name of the conda environment
-- inference_options (dict): Options for the inference pipeline module, including:
-    - model_name_formatter (str): Format string for the model name, e.g. "model_{timestamp}"
-    - fallback_username (str): User ID to use if user ID not found (default: 'generic_user')
-    - timestamp_column_name (str): Name of the timestamp column in the input data
-    - batching_options (dict): Options for batching the data, including:
+        - `model_name_formatter` (str): Format string for the model name, e.g. "model_{timestamp}"
+        - `experiment_name_formatter` (str): Format string for the experiment name, e.g. "experiment_{timestamp}"
+        - `timestamp_column_name` (str): Name of the timestamp column used in the data
+        - `conda_env` (dict): Conda environment settings, including:
+            - `channels` (list): List of channels to use for the environment
+            - `dependencies` (list): List of dependencies for the environment
+            - `pip` (list): List of pip packages to install in the environment
+            - `name` (str): Name of the conda environment
+- `inference_options` (dict): Options for the inference pipeline module, including:
+    - `model_name_formatter` (str): Format string for the model name, e.g. "model_{timestamp}"
+    - `fallback_username` (str): User ID to use if user ID not found (default: 'generic_user')
+    - `timestamp_column_name` (str): Name of the timestamp column in the input data
+    - `batching_options` (dict): Options for batching the data, including:
       [omitted for brevity]
-    - cache_dir (str): Directory to cache the rolling window data
-    - detection_criteria (dict): Criteria for filtering detections, such as threshold and field_name
-    - inference_options (dict): Options for the inference module, including model settings and other configurations
-    - num_output_ports (int): Number of output ports for the module
-    - preprocessing_options (dict): Options for preprocessing the data, including schema and timestamp column name
-    - stream_aggregation_options (dict): Options for aggregating the data by stream, including:
-        - aggregation_span (int): The time span for the aggregation window, in seconds
-        - cache_to_disk (bool): Whether to cache the aggregated data to disk
-    - user_splitting_options (dict): Options for splitting the data by user, including:
+    - `cache_dir` (str): Directory to cache the rolling window data
+    - `detection_criteria` (dict): Criteria for filtering detections, such as threshold and field_name
+    - `inference_options` (dict): Options for the inference module, including model settings and other configurations
+    - `num_output_ports` (int): Number of output ports for the module
+    - `preprocessing_options` (dict): Options for preprocessing the data, including schema and timestamp column name
+    - `stream_aggregation_options` (dict): Options for aggregating the data by stream, including:
+        - `aggregation_span` (int): The time span for the aggregation window, in seconds
+        - `cache_to_disk` (bool): Whether to cache the aggregated data to disk
+    - `user_splitting_options` (dict): Options for splitting the data by user, including:
       [omitted for brevity]
-    - write_to_file_options (dict): Options for writing the detections to a file, such as filename and overwrite
+    - `write_to_file_options` (dict): Options for writing the detections to a file, such as filename and overwrite
       settings
 
 ### Example JSON Configuration