SIENTIAPDE-1182

Remove Docker configuration files and refactor project structure

- Deleted docker-compose.yml and Dockerfile as part of the project restructuring.
- Updated README.md to reflect changes in project setup and configuration.
- Introduced a new __init__.py file in the laborious package to provide an overview of the system.
- Enhanced documentation across various modules, including metrics, activities, and workflows, to improve clarity and usability.
- Added comprehensive docstrings and comments to key classes and methods for better maintainability.

laborious/workflows/minimal_retrain.py

@@ -9,31 +9,53 @@ with workflow.unsafe.imports_passed_through():
 
 @workflow.defn(name="minimal_retrain")
 class MinimalRetrain():
+    """
+    Automated model retraining workflow for the Laborious system.
+
+    This workflow implements a complete model retraining pipeline that loads
+    training data, executes model retraining, updates production models,
+    and maintains comprehensive audit trails. It's designed for automated
+    model lifecycle management with minimal manual intervention.
+
+    The workflow provides a robust retraining process with:
+    - Automated data loading from configured data sources
+    - MLFlow model retraining with quality validation
+    - Production model updates with version control
+    - Comprehensive reporting and audit trail maintenance
+    - Error handling and notification integration
+    """
+
     @workflow.run
     async def run(self, input_data: dict[str, Any]):
         """
-        This workflow runs a minimal retrain of a model.
+        Execute the automated model retraining workflow.
 
-        The workflow executes in four steps:
-        1. Loads the data from the database
-        2. Formats the data and perform the retrain
-        3. Updates the production model
-        4. Saves a model
+        This method orchestrates the complete model retraining process by:
+        1. Loading training data using the provided custom SQL query
+        2. Executing MLFlow model retraining with the loaded data
+        3. Updating production models with newly trained versions
+        4. Persisting comprehensive retraining reports to database
+
+        The method implements comprehensive error handling and ensures all
+        required parameters are properly configured before proceeding.
 
         Args:
-            - input_data (dict[str, Any]): The input data for the workflow.
-                - schedule_name (str): The name of the schedule.
-                - model_name (str): The name of the model.
-                - model_id (int): The id of the model.
-                - query (str): The SQL query to be executed to load data.
-                - schema (dict, optional): The schema to store the report.
-                - table_name (str, optional): The name of the table to store report.
+            input_data: Complete configuration for the retraining workflow
+                Required keys:
+                    - schedule_name (str): Schedule identifier for the retraining
+                    - model_name (str): Name of the ML model to retrain
+                    - model_id (int): Unique identifier for the model version
+                    - query (str): SQL query for training data loading
+                    - schema (str, optional): Database schema for report storage
+                    - table_name (str, optional): Target table for retraining reports
+                    - datetime_columns (list[str], optional): Columns to treat as datetime
 
         Returns:
-            None
+            None: The workflow completes successfully when all steps finish
 
         Raises:
-            Exception: If any of the required parameters are missing or if the workflow fails.
+            Exception: If any required parameters are missing or if the workflow fails
+                during data loading, retraining, or model update operations
         """
 
         metadata = {