3 Tips for Feature Engineering

First you start with solid domain knowledge – understanding the business or dataset context helps you design features that reflect real-world signals and improve model interpretability. Second, use feature templates to standardise workflows, including time-based aggregates, interactions, and categorised bins to capture patterns effectively. Third, automate your feature pipelines—not just to scale but to ensure consistency and reliability across training and inference pipelines. These tips balance creativity and structure, making your models both powerful and maintainable. Boost your feature-engineering approach with clarity and efficiency, with Maddie Dierkes‘ post today!