Dealing with a messy prescriptive monolith. How do you survive this? [D]
📰 Reddit r/MachineLearning
Learn to survive a messy prescriptive monolith by breaking it down into manageable parts and applying software engineering principles
Action Steps
- Identify the different components of the monolith, such as data ingestion and model training, and separate them into distinct modules
- Apply the Single Responsibility Principle to each module to improve maintainability
- Use version control and branching to manage changes and collaborate with others
- Implement automated testing and continuous integration to ensure code quality
- Refactor the codebase to follow a more modular and scalable architecture
Who Needs to Know This
Data scientists and machine learning engineers who inherit legacy projects can benefit from this advice to improve code quality and maintainability
Key Insight
💡 Separating concerns and applying software engineering principles can help make a messy monolith more maintainable
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💡 Taming a messy prescriptive monolith: break it down, separate concerns, and apply software engineering principles #MachineLearning #SoftwareEngineering
Key Takeaways
Learn to survive a messy prescriptive monolith by breaking it down into manageable parts and applying software engineering principles
Full Article
Months ago, I got my first maintenance project. Before this, I had only built new solutions from scratch and maintained my own code. But maintaining someone else's system feels completely different. It’s a prescriptive recommendation system that uses XGBoost models and Differential Evolution for optimization. The problem is that everything is in a single repository: raw data ingestion, transformations, model training, reporting, the optimization
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