What is the ideal structure for a machine learning project?

What is the ideal structure for a machine learning project?

A well-structured ML project typically includes directories for data, models, notebooks, and scripts.

Can you explain what each of these directories should contain?

Sure! The 'data' directory holds raw and processed datasets, 'models' contains trained models, 'notebooks' has Jupyter notebooks, and 'scripts' includes code for training and evaluation.

How do I manage dependencies in my ML project?

You can use a requirements.txt file or a conda environment to manage dependencies effectively.

What about version control for my models?

Using Git for version control is essential. You can also consider tools like DVC for tracking model versions.