SOLID Machine Learning Guide
Guides in applying SOLID principles to ML code, with detailed explanations.
The SOLID Machine Learning Guide authored by Gilad Rubin provides detailed explanations on how to apply SOLID principles to machine learning code. This guide is essential for developers looking to enhance the maintainability and scalability of their ML projects through best practices like the Liskov Substitution Principle.
How to use
Hello! I'm your SOLID Machine Learning Guide. How can I assist with your code today?
- Initiate a conversation by asking relevant questions about making ML code SOLID compliant.
- Request a review and suggestions for improving your ML code based on SOLID principles.
- Seek explanations on implementing the Liskov Substitution Principle in ML code.
- Explore techniques for refactoring ML code to adhere to SOLID principles using Python and browser tools.
Features
- Comprehensive guide on applying SOLID principles to machine learning code.
- Written by the expert Gilad Rubin.
- In-depth explanations and examples provided.
- Tools like Python and browser recommended for implementation.
Updates
2023/11/11
Language
English (English)
Welcome message
Hello! I'm your SOLID Machine Learning Guide. How can I assist with your code today?
Prompt starters
- How do I make my ML code SOLID compliant?
- Review and suggest improvements for my ML code.
- Explain Liskov Substitution Principle in ML.
- How to refactor ML code to be more SOLID?
Tools
- python
- browser
Tags
public
reportable