Refactored Diamond Pricing Model with MLOps Integration

2 min read

Refactored Diamond Pricing Model with MLOps Integration

  • Day: 2024-04-12
  • Time: 18:10 to 19:00
  • Project: Dev
  • Workspace: WP 2: Operational
  • Status: In Progress
  • Priority: MEDIUM
  • Assignee: Matías Nehuen Iglesias
  • Tags: Mlops, Machine Learning, Project Structure, Api Integration, SGD, Bash Scripting

Description

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.

Evidence

  • source_file=2024-04-12.sessions.jsonl, line_number=0, event_count=0, session_id=cfcfeb683a03fc020e007eb48da6cb8c68e9381c59c5d0430a567c9131141c27
  • event_ids: []

Graph View

Backlinks