Beyond Deployment: Continuously Adding Features to Drive Marginal Gains in Models

Machine learning models are never truly “done.” As data evolves, so should the models that rely on it. But how can we ensure continuous improvement without costly retraining or manual intervention? In this talk, we introduce an automated pipeline designed to incrementally enhance model performance by systematically testing and integrating new features.

Data Orchestration Tools, Data Pipelines, Machine Learning

Machine learning models are never truly “done.” As data evolves, so should the models that rely on it. But how can we ensure continuous improvement without costly retraining or manual intervention? In this talk, we introduce an automated pipeline designed to incrementally enhance model performance by systematically testing and integrating new features. Using Deep Feature Synthesis (DFS), we generate a dynamic pool of candidate features and evaluate their impact on predictive power. Only features that demonstrably improve the model are added, ensuring continuous refinement without unnecessary complexity. This process transforms model performance monitoring from a passive task into an active, value-driven strategy. Attendees will learn:

• How to automate feature selection and model enhancement at scale.
• The role of DFS in discovering new predictive signals.
• Best practices for integrating incremental feature addition into production workflows.