Best Practices for Coding in ML/DS - Techniques to Construct your Project
summary
Advice targets long‑lived ML projects: invest time upfront so future changes stay fast, enforce single responsibility principle and DRY, prefer descriptive names over one‑letter variables, minimize comments by using docstrings and type hints, and keep reproducible environments with pinned packages via requirements.txt or setup.py and virtualenvs. Move experiments out of notebooks into .py within 24 hours, use version control and structured commits, use logging not prints, and record experiments with tools like Neptune.ai, Weights & Biases or Aim for searchable, comparable results.
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chapters (16)
- 0:00ML/DS project best practices
- 1:56Slow coding for long-term efficiency
- 3:24Poor code in ML consequences
- 8:43Clear variable names
- 10:35When to comment code
- 12:42Docstrings and type hints
- 15:55Jupyter notebooks problems
- 18:25IDE for ML development
- 21:56PyCharm Dataspell for coding
- 23:21Version control importance
- 24:26Reproducible experiments
- 25:51Debugging and logging
- 27:18Logging ML tools
- 28:40ML project design principles
- 31:48Iterative thinking in ML
- 32:11Q&A
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