Apply reproducibility and traceability practices in data engineering workflows
Write clean, production-ready notebooks beyond exploratory development
Handle training challenges such as rare events and imbalanced datasets
Analyze model results and apply dataset balancing techniques
Select appropriate deployment modes and build reproducible environments
Define useful monitoring metrics and choose relevant monitoring frameworks
