📅 2024-04-12 — Session: Refactored Diamond Pricing Model with MLOps Integration
🕒 18:10–19:00
🏷️ Labels: Mlops, Machine Learning, Project Structure, Api Integration, Bash
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The session aimed to refactor the diamond pricing model by integrating MLOps principles to enhance the project’s efficiency, scalability, and maintainability.
Key Activities
- Explored the use of Stochastic Gradient Descent (SGD) for predicting diamond prices, focusing on feature scaling, handling categorical features, and hyperparameter tuning.
- Developed a comprehensive strategy for refactoring the machine learning project by integrating MLOps principles, including data management and model deployment.
- Outlined a structured approach to organizing the project, emphasizing modular file architecture and effective use of Jupyter notebooks.
- Analyzed the current project structure and provided actionable recommendations for improvement.
- Revised the project file architecture to integrate an API for model interaction, detailing the new API directory and its components.
- Utilized Bash commands to search for path separators in project files and analyzed path dependencies among them.
Achievements
- Established a clear strategy for refactoring the diamond pricing model using MLOps principles.
- Defined a revised project structure that includes API integration for enhanced interaction and scalability.
Pending Tasks
- Implement the revised project structure and API integration.
- Continue refining the hyperparameters for the SGD model.
- Further analyze and optimize path dependencies in project files.