📅 2024-04-12 — Session: Refactored Diamond Pricing Model with MLOps Integration
🕒 18:10–19:00
🏷️ Labels: Mlops, Machine Learning, Project Structure, Api Integration, SGD, Bash Scripting
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The primary goal of this session was to refactor a diamond pricing model by integrating Machine Learning Operations (MLOps) principles to enhance its efficiency, scalability, and maintainability.
Key Activities
- Stochastic Gradient Descent (SGD) Implementation: Explored the use of SGD for predicting diamond prices, focusing on feature scaling, handling categorical features, and hyperparameter tuning.
- MLOps Principles: Developed a strategy for refactoring the project using MLOps principles, which included data management, model deployment, and user interface considerations.
- Project Structuring: Outlined a structured approach for organizing the machine learning project, emphasizing modular file architecture and effective use of Jupyter notebooks.
- Project Structure Analysis: Analyzed the current project structure and provided recommendations for improving organization and scalability.
- API Integration: Revised the project file architecture to integrate an API for model interaction, detailing the new API directory and components.
- Bash Scripting: Utilized bash scripting to search for path separators in project files, aiding in the analysis of path dependencies.
Achievements
- Successfully outlined a comprehensive refactoring strategy for the diamond pricing model using MLOps principles.
- Developed a revised project structure that supports API integration and enhances scalability.
- Provided actionable recommendations for improving project organization and maintainability.
Pending Tasks
- Implement the revised project structure and API integration in the production environment.
- Conduct further testing and validation of the refactored model.
- Continue monitoring and optimizing the model’s performance post-deployment.