Add Comprehensive Documentation for ML Lifecycle Pipeline

[greemaltend4]

Feature Request: Enhanced Documentation for ML Lifecycle Pipeline

Description

The current README provides an overview of the ProfitScout ML Pipeline, but the documentation for the ML Lifecycle components appears to be sparse. Given the importance of understanding the ML lifecycle for potential contributors and users, I suggest adding comprehensive documentation that covers the following areas:

1. Detailed Explanation of Each Phase: For example, outline the steps involved in model training, registration, and batch prediction. Provide example inputs and expected outputs for each phase.
2. Code Walkthrough: Include a walkthrough of the main components, especially the create_training_pipeline.py, create_inference_pipeline.py, and create_hpo_pipeline.py files, explaining their functionalities and how they interact within the ML lifecycle.
3. Visual Aids: Enhance the architecture diagram with annotations to illustrate how data flows through the ML lifecycle. This could help in making complex interactions clearer.
4. Sample Use Cases: Provide example scenarios for users to better understand how to utilize the ML lifecycle pipeline effectively in their own projects.

Benefits

Enhancing the documentation in these areas will not only improve onboarding for new contributors but also foster a better understanding of the entire project. This, in turn, can encourage more community involvement and contributions.

Thank you for considering this request!