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/__init__.py

@@ -0,0 +1,82 @@
+"""
+Sientia DataOps Laborious Package
+
+A high-performance, scalable machine learning prediction system built on Temporal.io
+for industrial data processing and ML model inference. The Laborious system provides
+enterprise-grade ML model management, batch prediction processing, and real-time
+data export capabilities.
+
+Package Overview:
+    The Laborious package implements a comprehensive ML workflow orchestration
+    system that integrates with MLFlow for model management, PostgreSQL for data
+    storage, and OPC servers for real-time industrial data export.
+
+Key Components:
+    - activities: Temporal activity implementations for ML operations
+    - workflows: Temporal workflow definitions for prediction orchestration
+    - worker: Main worker implementation for workflow execution
+    - utils: Utility functions and configuration management
+    - metrics: Prometheus metrics for monitoring and observability
+
+Main Features:
+    - Batch prediction processing using MLFlow models
+    - Data quality validation and filtering
+    - Real-time data export to OPC servers
+    - PostgreSQL data persistence
+    - Comprehensive monitoring and metrics
+    - Automatic retry policies and error handling
+
+Architecture:
+    The system uses Temporal.io for workflow orchestration with clear separation
+    of concerns between data loading, ML operations, quality validation, and
+    data export. It supports multiple OPC servers and implements configurable
+    data quality gates throughout the prediction pipeline.
+
+Example Usage:
+    >>> from laborious.worker.worker import main
+    >>> import asyncio
+    >>> 
+    >>> # Start the Laborious worker
+    >>> asyncio.run(main())
+    
+    >>> # Or use specific components
+    >>> from laborious.activities.activities import Activities
+    >>> from laborious.workflows.predictions_batch import PredictionsBatch
+
+Dependencies:
+    - temporalio: Temporal workflow orchestration
+    - psycopg2-binary: PostgreSQL database adapter
+    - sqlalchemy: Database ORM and connection management
+    - asyncua: OPC UA client implementation
+    - redis: Caching and session management
+    - prometheus-client: Metrics collection and export
+
+Environment Configuration:
+    The system is configured through environment variables for database
+    connections, MLFlow servers, OPC servers, and other external services.
+    See the README.md for complete configuration documentation.
+
+License:
+    This project is licensed under the terms specified in the LICENSE file.
+
+For more information, see the project README.md and documentation.
+"""
+
+__version__ = "0.4.4"
+__author__ = "Sientia DataOps Team"
+__description__ = "ML prediction system built on Temporal.io for industrial data processing"
+__keywords__ = ["machine-learning", "temporal", "mlflow", "opc", "postgresql", "industrial"]
+__url__ = "https://github.com/Aignosi/sientia-dataops-laborious"
+
+# Import key components for easy access
+from . import metrics
+from . import activities
+from . import workflows
+from . import worker
+
+__all__ = [
+    "metrics",
+    "activities", 
+    "workflows",
+    "worker"
+]