Best practices for building an end‑to‑end ML pipeline for tabular data

19 Eki 18:00

I'm planning to set up a production‑ready machine learning workflow for a large tabular dataset. The data will go through cleaning, feature engineering, model selection, hyper‑parameter tuning, and monitoring. I want a modular, reproducible pipeline that can be versioned and scaled. What are the key steps and tools you usually include? How do you handle data drift and model retraining without locking into a specific platform? Any recommended patterns or pitfalls to avoid?