When ML models underperform due to data issues, aligning engineering and optimization goals starts with diagnosing the root causes. Bring the data and engineering teams together to analyze the problem—whether it's data quality, missing values, or insufficient diversity. Set a shared goal of improving data pipelines, like enhancing preprocessing steps, automating validation, or sourcing better training data. At the same time, model optimization should be adjusted to focus on realistic, data-driven metrics. Regular cross-team check-ins help ensure alignment, so both teams work toward solutions that simultaneously improve data integrity and model performance.

When machine learning models underperform, data issues are often the root cause. Here’s how I approached tackling data issues affecting our ML models: 1) Diagnose Problems: We audited our data pipelines to find bottlenecks and inconsistencies. 2) Align Goals: We prioritized both data integrity and model performance to ensure all teams were on the same page. 3) Enhance the Pipeline: Validation checks, automated monitoring, and error handling were added to ensure reliable data. 4) Optimize Models: Cleaner data led to retraining the models, resulting in significant performance improvements. 5) Set Feedback Loops: Real-time monitoring and alerts were implemented to quickly catch and resolve new issues.